KR20200041341A - Network location detection - Google Patents

Abstract

Systems and methods for detecting the presence of a body in a network devoid of fiducial elements using signal forward and back scattering of radio frequency (RF) signals and signal absorption caused by the presence of biological masses in a communication network.

Description

Location detection in the network

Cross reference to related applications

This application is a continuing application of US Utility Patent Application No. 15 / 713,309 filed on September 22, 2017, and a continuing application of United States Utility Patent Application No. 15 / 713,219 filed on September 22, 2017; Continued application of US Utility Patent Application No. 15 / 674,487 filed on August 10, 2017; And US Utility Application No. 15 / 674,328, filed on August 10, 2017. Application 15 / 713,219 is a continuing application of U.S. Utility Application No. 15 / 674,328 filed on August 10, 2017, which is a partial continuing application of U.S. Utility Application No. 15 / 600,380 filed on May 19, 2017, which Continued filing of US Utility Patent Application No. 15 / 227,717 filed on August 3, 2016, which is filed on Provisional Patent Application No. 62 / 262,954 filed on November 9, 2015 and United States filed on September 16, 2015 Claim the benefit of provisional patent application number 62 / 219,457. Application 15 / 713,309 is a continuing application of United States Utility Patent Application No. 15 / 674,487 filed on August 10, 2017, which is a continuing application of United States Utility Patent Application No. 15 / 674,328 filed on August 10, 2017, which Partially continued application of United States Utility Patent Application No. 15 / 600,380 filed May 19, 2017, which is a continuing application of United States Utility Application No. 15 / 227,717 filed on August 3, 2016, which is November 9, 2015 Claims the benefit of U.S. Provisional Patent Application No. 62 / 252,954 filed on one day and U.S. Provisional Patent Application No. 62 / 219,457 filed on September 16, 2015. Application 15 / 227,717 filed on March 29, 2016 and filed in the United States Continued filing of U.S. Utility Patent Application No. 15 / 084,002 published on October 18, 2016 as patent number 9,474,042, these applications eventually also being filed in U.S. Provisional Patent Application Nos. 62 / 252,954 and 2015 United States Provisional Patent Application No. filed on September 16, It claims the benefit of 62 / 219,457. The entire disclosure of these documents is incorporated herein by reference.

Field of invention

The present disclosure relates to the field of object detection, and more particularly, to a system and method for detecting the presence of a biological mass in a wireless communication network.

Object tracking can be performed using a number of techniques. For example, a mobile transceiver can be attached to the object. Examples of such systems include global positioning systems such as GPS that use orbiting satellites to communicate with terrestrial transceivers. However, such a system is generally less effective in a room where satellite signals are blocked and the accuracy is poor. Therefore, other technologies such as Bluetooth (TM) beacons that calculate the location of roaming or unknown transceivers are frequently used indoors. The roaming transceiver serves as a fiducial element.

These systems have some drawbacks, and among them, the object being tracked must contain a transceiver. In certain applications, the object to be tracked will either have no such origin element or will actively disable any such element, such as a home intruder.

There are other techniques that can detect and track objects without using origin elements. Radar, for example, is a useful object detection system that uses RF waves to determine the range, angle, or speed of objects, including aircraft, ships, spacecraft, guided missiles, automobiles, weather formations, and terrain. Radars typically operate by transmitting electromagnetic waves using waves on the radio frequency ("RF") of the electromagnetic spectrum reflected from any object in their path. A receiver, typically part of the same system as the transmitter, receives and processes these reflected waves to determine the characteristics of the object. Other systems similar to radars that use different parts of the electromagnetic spectrum can also be used in a similar way, such as ultraviolet, visible or near infrared light from a laser.

Radar technology does not require an origin element, but has other drawbacks. For example, radar signals are susceptible to signal noise, or random fluctuations of the signal caused by internal electrical components, as well as noise and interference from external sources such as natural background radiation. Radar is also vulnerable to external interference sources, such as inclusions that block the beam path, and can be deceived by objects of specific size, shape and orientation.

The following is a summary of the invention to provide a basic understanding of some aspects of the invention. This summary is not intended to identify key or critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concepts of the invention in a simplified form as a prelude to the more detailed description that is presented later.

Due to these and other problems in the art, in this application, among other things, a method for detecting the presence of a human body is described, the method comprising: providing a first transceiver disposed at a first location within a detection area; Providing a second transceiver disposed at a second location in the detection area; A computer server communicatively coupled to the first transceiver; The first transceiver receives a first set of wireless signals from the second transceiver via the wireless communication network; The computer server receives a first set of signal data from the first transceiver, the first set of signal data includes data regarding characteristics of the first set of wireless signals, and the characteristic data is the communication network Is generated as part of the normal operation of the first transceiver on; The computer server generates a baseline signal profile for communication from the second transceiver to the first transceiver, the baseline signal profile based at least in part on the characteristics of the wireless signal in the first set of signal data ; Indicating characteristics of radio transmission from the second transceiver to the first transceiver when no human body is present in the detection area; The first transceiver receives a second set of wireless signals from the second transceiver via the wireless communication network; The computer server receives a second set of signal data from the first transceiver, the second set of signal data includes data regarding the characteristics of the second set of wireless signals, and the characteristic data is the communication network Is generated as part of the normal operation of the first transceiver on; And the computer server determines if a human body is present in the detection area, and the determination is based at least in part on comparing the radio signal characteristics in the received second set of radio signal data to the baseline signal profile.

In an embodiment of the method, the first set of signal characteristics includes a wireless network signal protocol characteristic determined by the first transceiver.

In another embodiment of the method, the wireless network signal protocol characteristics are selected from the group consisting of received signal strength, latency and bit error rate.

In another embodiment of the method, the method comprises: Providing a third transceiver disposed at a third location within the detection area; The first transceiver receives a third set of radio signals from the third transceiver via the wireless communication network; The computer server receives a third set of signal data from the first transceiver, the third set of signal data includes data regarding the characteristics of the third set of wireless signals, and the characteristic data is the communication network Is generated as part of the normal operation of the first transceiver on; The computer server generates a second baseline signal profile for communication from the third transceiver to the first transceiver, and the second baseline signal profile is at least a characteristic of the radio signal in the third set of signal data. Based in part; And indicating characteristics of radio transmission from the third transceiver to the first transceiver when no human body is present in the detection area. The first transceiver receives a fourth set of wireless signals from the third transceiver via the wireless communication network; The computer server receives a fourth set of signal data from the first transceiver, the fourth set of signal data includes data relating to the characteristics of the fourth set of wireless signals, and the characteristic data is the communication network Is generated as part of the normal operation of the first transceiver on; And the computer server is based at least in part on comparing wireless signal characteristics in the received fourth set of wireless signal data with the second baseline signal profile in a determining step of determining whether a human body is present in the detection area. do.

In another embodiment of the method, the determining step applies a statistical method to the second set of radio signal data to determine the presence of the human body.

In another embodiment of the method, the method comprises the computer server continuously determining the presence or absence of a human body within a detection area, the determination being continuously received at the computer server from the baseline signal profile and the first transceiver. Based at least in part on comparison with signal data comprising data relating to the characteristics of the first set of wireless signals; And the computer continuously updating the baseline signal profile based on the continuously received signal data when the continuously received signal data indicates that there is no human body in the detection area.

In another embodiment of the method, the method further comprises the computer server determining whether the number of human bodies is within the detection area, the decision being made to compare the received second set of signal characteristics with the baseline signal profile. It is based at least in part.

In another embodiment of the method, the method further comprises the computer server determining the location of one or more human bodies within the detection area, the determination at least for comparison of the received second set of signal characteristics and the baseline signal. It is based in part.

In another embodiment of the method, the method further includes a computer server operatively coupled to the second system, and only after the computer server detects the presence of a human body in the detection area, the computer operates the second system. .

In another embodiment of the method, the detection network and the second system are configured to communicate using the same communication protocol.

In another embodiment of the method, the second system is an electrical system.

In another embodiment of the method, the second system is a lighting system.

In another embodiment of the method, the second system is a heating, exhaust and cooling (HVAC) system.

In another embodiment of the method, the second system is a security system.

In another embodiment of the method, the second system is an industrial automation system.

In another embodiment of the method, the wireless communication protocol is selected from the group consisting of Bluetooth (TM), Bluetooth (TM) low energy, ANT, ANT +, WiFi, Zigbee, Thread and Z-Wave.

In another embodiment of the method, the wireless communication network has a carrier frequency in a range covering 850 MHz and 17.5 GHz.

In another embodiment of the method, determining whether a human body is within the detection area is determining a first sample position of the human body having a fiducial element in the detection area, wherein the first sample position is the origin Determining the first sample location, which is determined based on detection of the element; Determining a second sample position of the human body in the detection area, the second sample position being at least partially in comparison of the received second set of signal data and the baseline signal profile without using the fiducial element Determining the second sample position, which is determined based on the; Comparing the first sample position and the second sample position; And adjusting the determining step based on a non-fiducial element position to improve the position calculation capability of the system, wherein the adjustment is based on the comparing step, the adjusting step Adjusted based on learning.

In another embodiment of the method, determining whether the human body is present in the detection area is determined based on user input or behavior, determining that the human body is present in the area, wherein the sample signal property is at least partially in line with the baseline signal property of the empty space. When corresponding to, modifying at least partially the baseline signal characteristics for the empty space; Modifying at least partially a signal characteristic associated with the occupied space; And adjusting how the sample signal characteristic is compared with the baseline and other comparative signal characteristics to improve the accuracy of the system over time.

In embodiments of the present system, user input or actions that provide presence data are provided directly to the system in any form, including but not limited to physical switches, smart phone inputs or auditory cues.

In embodiments of the present system, user input or action providing presence data provides such changes during a dimming step in the lighting system, such as intentionally changing the signal profile intentionally to respond to a decision made by the system. It is provided indirectly to the system in some form, such as.

In another embodiment of the method, the method further includes the computer server storing a plurality of history data records indicating whether the human body has been in the detection area for a period of time, wherein each history data record is detected in the detected area. Includes an indication of the date and time that the number of human bodies was detected in the number of human bodies and the detection area; And the computer server makes history data recording available to one or more external computer systems through an interface.

Above all, the present application also describes a method for detecting the presence of a human body, the method comprising: providing a first transceiver disposed at a first position in a detection area; Providing a second transceiver disposed at a second location in the detection area; A computer server communicatively coupled to the first transceiver; Providing a first external system operatively coupled to the computer server; Providing a second external system operatively coupled to the computer server; The computer server may include a baseline signal data set including characteristic data regarding signal characteristics of a first set of radio signals received by the first transceiver from the second transceiver when no human body is present in the detection area. Receiving from a first transceiver, the characteristic data is generated as part of the normal operation of the first transceiver on the communication network; The computer server generates a baseline signal profile for communication from the second transceiver to the first transceiver when a human body does not exist in the detection area, and the baseline signal profile does not include a human body in the detection area. Otherwise, based at least in part on characteristic data indicating characteristics of wireless transmission from the second transceiver to the first transceiver; The computer server receives the first set of baseline signal data including characteristic data regarding the signal characteristics of the second set of radio signals received by the first transceiver from the second transceiver when a human body is present in the detection area. Receiving from the first transceiver, the characteristic data being generated as part of the normal operation of the first transceiver on the communication network; The computer server generates a first sample baseline signal profile for communication from the second transceiver to the first transceiver when a human body is present in the detection area, and the first sample baseline signal profile is in the detection area. Based at least in part on characteristic data in the first set of sample baseline signal data representing characteristics of wireless transmission from the second transceiver to the first transceiver when a human body is present; The computer server receives a second set of baseline signal data including characteristic data relating to signal characteristics of a third set of wireless signals received by the first transceiver from the second transceiver when a human body is present in the detection area. Receiving from the first transceiver, the characteristic data being generated as part of the normal operation of the first transceiver on the communication network; The computer server generates a second sample baseline signal profile for communication from the second transceiver to the first transceiver when a human body is present in the detection area, and the second sample baseline signal profile is located in the detection area. Based at least in part on characteristic data in the second set of sample baseline signal data representing characteristics of wireless transmission from the second transceiver to the first transceiver when a human body is present; The computer server receives a third set of baseline signal data including characteristic data relating to signal characteristics of a fourth set of radio signals received by the first transceiver from the second transceiver when a human body is present in the detection area. Receiving from the first transceiver, the characteristic data being generated as part of the normal operation of the first transceiver on the communication network; The computer server determines to operate the first external system based on the computer server determining that the characteristic data of the third set of sample baseline signal data corresponds to the first sample baseline signal profile; Determining that the computer server does not operate the second external system based on the computer server determining that the characteristic data of the third set of sample baseline signal data corresponds to the second sample baseline signal profile. Includes.

In an embodiment of the method, the determination of operating the first external system and the determination of not operating the second external system comprises determining a first sample position of the human body having a fiducial element in the detection area. , Determining the first sample location, wherein the first sample location is determined based on detection of the fiducial element; Determining a second sample position of the human body in the detection area, the second sample position being at least partially in comparison of the received second set of signal data and the baseline signal profile without using the fiducial element Determining the second sample position, which is determined based on the; Comparing the first sample position and the second sample position; And adjusting the determining step based on the non-fiducial element position to improve the system's position calculation capability, wherein the adjustment is based on the comparing step. Adjusted based on machine learning.

In another embodiment of the method, the determination of operating the first external system and the determination of not operating the second external system are steps of determining the first sample position of the human body in the detection area using inference, wherein the first Determining a first sample location, wherein a sample location is determined based on detecting a human body interacting with the system in some known manner; Determining a second sample position of the human body in the detection area, wherein the second sample position is at least for comparison of the received second set of signal data and the baseline signal profile without using the estimated position Determining the second sample location, which is determined based in part on; Comparing the first sample position and the second sample position; And adjusting the determining step based on the speculative position to improve the system's position calculation capability, wherein the adjustment is adjusted based on machine learning, the adjusting step being based on the comparing step.

In another embodiment of the method, the characteristic data relating to the wireless signal includes data relating to signal characteristics selected from the group consisting of received signal strength, latency and bit error rate.

In another embodiment of the method, the computer server generates a first sample baseline signal profile by applying a statistical method to the first set of sample baseline signal data, and the computer server generates a second set of sample baseline signal data. A second sample baseline signal profile is generated by applying a statistical method.

In another embodiment of the method, the method further comprises: a set of baseline signal data, wherein the computer server includes characteristic data relating to signal characteristics of a second? Wireless signal received by the first transceiver from the second transceiver. Is received from the first transceiver, and the characteristic data is generated by the first transceiver as part of the normal operation of the first transceiver on a communication network, and the computer server continuously receives the set of baseline signal data received. Further comprising updating the baseline signal profile based on the additional set of baseline signal data that is continuously received when indicating that there is no human body in the detection area.

In another embodiment of the present method, the method is characterized in that the computer server has characteristic data relating to signal characteristics of a second set of radio signals received by the first transceiver from the second transceiver when there is more than one human body within the detection area. Receiving a signal data set comprising from the first transceiver, the characteristic data being generated as part of the normal operation of the first transceiver on the communication network; The computer server further includes determining the quantity of humans present in the detection region based at least in part on the comparison of the signal data set and the baseline signal profile.

In another embodiment of the method, the method further comprises the computer server determining the location of each of the one or more human bodies present in the detection area, the determination being at least in comparison to a set of signal data and a baseline signal profile. It is based in part.

In another embodiment of the method, when the human body is in the detection area, the computer server determines that the human body is in the detection area and the characteristic data of the third set of sample baseline signal data corresponds to the second sample baseline signal profile Even if it does, it operates the first external system.

In another embodiment of the method, when the human body is in the detection area, the computer server determines that the human body is in the detection area and the characteristic data of the third set of sample baseline signal data corresponds to the second sample baseline signal profile Only the second external system is operated.

In another embodiment of the method, the wireless communication network has a carrier frequency in a range covering 850 MHz and 17.5 GHz.

In another embodiment of the method, the computer server stores a plurality of history data records indicating whether the human body has been in the detection area for a period of time, and each of the history data records is configured to detect the human body detected in the detection area. Each of the number and the number of the human body includes an indication of the date and time detected in the detection area; And the computer server making the history data record available to one or more external computer systems through an interface.

1 is a schematic diagram of an embodiment of a system according to the present disclosure.
2 is a flowchart of an embodiment of a method according to the present disclosure.
3A shows a schematic diagram of a system for detecting changes in a detection network over time according to the present disclosure.
3B shows a schematic diagram of a system for detecting changes in the position of a human body in a detection network over time according to the present disclosure.

The following detailed description and disclosure are illustrated by way of example and not limitation. This description will enable those skilled in the art to make and use the disclosed systems and methods, and will describe some embodiments, adaptations, modifications, alternatives, and uses of the disclosed systems and methods. Since various changes can be made in the above configuration without departing from the scope of the present disclosure, all matters included in the description or illustrated in the accompanying drawings should be interpreted as illustrative rather than restrictive.

Generally speaking, what is described in this application is a system and method for detecting the presence of a body in a network without an origin element. In general, the systems and methods described in this application use signal absorption of RF communication caused by the presence of biological masses in communication networks, generally mesh networks, and signal forward scattering and reflected backscatter. .

Throughout this disclosure, the term “computer” generally describes hardware that implements functions provided by digital computing technology, particularly computing functions related to microprocessors. The term “computer” is not limited to any particular type of computing device, and is intended to include any computer device, including but not limited to: processing devices, microprocessors, personal computers, desktop computers, laptop computers, work Stations, terminals, servers, clients, portable computers, handheld computers, smart phones, tablet computers, mobile devices, server farms, hardware appliances, mini computers, mainframe computers, video game consoles, handheld video game products And wearable computing devices including, but not limited to, eyewear, wrist wear, pendants, and clip-on devices.

As used herein, “computer” is an abstract concept of functionality provided by a single computer device with typical hardware and accessories of a computer that plays a specific role. By way of example and not limitation, it will be understood by those skilled in the art that the term “computer” in connection with a laptop computer includes the functionality provided by a pointer-based input device such as a mouse or track pad, while an enterprise-class server and It will be understood by those skilled in the art that the term “computer” as used in conjunction with includes features provided by redundant systems such as RAID drives and dual power supplies.

It is well known to those skilled in the art that the functionality of a single computer can be distributed across multiple individual machines. This distribution can function where a particular machine performs a particular task; Or, if each machine can perform most or all functions of any other machine, and tasks are assigned based on the resources available at a particular point in time, balance is achieved. Thus, the term “computer” as used herein refers to a single, including, but not limited to, network server farms, “cloud” computing systems, software-as-a-services, or other distributed or collaborative computer networks. It may refer to a standalone self-contained device or a plurality of machines operating together or independently.

Those skilled in the art also recognize that some devices that are not normally considered "computers" exhibit the characteristics of "computers" in certain situations. When such a device performs the function of the "computer" described in the present application, the term "computer" includes such a device to such a degree. Devices of this type include, but are not limited to, network hardware, print servers, file servers, NAS and SANs, load balancers and any other hardware capable of interacting with the systems and methods described herein in connection with conventional “computers”. It is not limited. "

Throughout this disclosure, the term “software” may be executed by a computer processor, or by a computer processor, including but not limited to a virtual processor, or by use of a runtime environment, virtual machine and / or interpreter. Code objects, program logic, instruction structures, data structures and definitions, source code, executable and / or binary files, machine code, object code, compiled libraries, implementations, algorithms, libraries, or any that can be converted into any form Refers to an instruction or set of instructions. Those skilled in the art recognize that software is not limited to microchips, can be wired or embedded in hardware, and is still considered "software" within the meaning of the present disclosure. For the purposes of the present disclosure, the software is without limitation: RAM, ROM, flash memory BIOS, CMOS, motherboard and daughter board circuit, hardware controller, USB controller or host, peripheral devices and controller, video card, audio controller , Network card, Bluetooth and other wireless communication devices, virtual memory, storage devices and related controllers, firmware and device drivers. The systems and methods described in this application are typically contemplated using computers and computer or machine readable storage media or computer software stored in memory.

Throughout this disclosure, the terms used in this application to describe or refer to software holding media, including, but not limited to, terms such as “medium”, “storage medium” and “memory” are temporary such as signals and carriers. Media may be included or excluded.

Throughout this disclosure, the term “network” generally refers to a voice, data, or other telecommunication network in which computers communicate with each other. The term “server” generally refers to a computer that provides services over a network, and “client” generally refers to a computer that accesses or uses services provided by a server over a network. Those skilled in the art will understand that the terms "server" and "client" may refer to hardware, software or a combination of hardware and software depending on the situation. Those skilled in the art will further understand that the terms “server” and “client” can refer to an endpoint of a network communication or network connection, including but not limited to a network socket connection. Those skilled in the art will further understand that a "server" may include a plurality of software and / or hardware servers carrying a service or set of services. A person skilled in the art may refer to a network communication in the form of a noun in the form of a noun or an endpoint of a network (eg, a “remote host”), or a server providing a service through a network in the form of a verb (“website hosting”) ) Or will further understand that it may refer to an access point of a service over a network.

Throughout the present disclosure, the term “real time” refers to software in which a given event can be started or completed or a given module, software, or system is operating within an operating deadline to respond, generally responding or It is called that the performance time is within the normal user's perception, and in fact taking into account the technical situation, in fact, occurs simultaneously with the reference event. A person skilled in the art, "real time" does not literally mean that the system processes and / or responds to input immediately, but the system processes it fast enough that the processing or response time is within the general human perception of real-time delivery in the operating environment of the program. And / or understands to respond. Those skilled in the art understand that when the operating environment is a graphical user interface, “real time” generally refers to a real time response time of 1 second or less, and milliseconds or microseconds are preferred. However, one of ordinary skill in the art understands that systems operating in "real time" in different operating environments may exhibit delays longer than 1 second, particularly when network operation is involved.

Throughout this disclosure, the term “transmitter” refers to a set of equipment or equipment having hardware, circuitry and / or software for generating and transmitting electromagnetic waves that carry messages, signals, data or other information. The transmitter may also include components that receive electrical signals including such messages, signals, data or other information and convert them into electromagnetic waves. The term "receiver" means a set of equipment or equipment having hardware, circuitry and / or software that receives such transmitted electromagnetic waves and converts them into generally electrical signals from which messages, signals, data or other information can be extracted. . The term "transceiver" generally refers to a device or system that includes both a transmitter and a receiver, such as a two-way radio, or wireless networking router or access point. For purposes of this disclosure, all three terms are to be understood as interchangeable unless otherwise indicated; For example, the term "transmitter" should be understood to mean the presence of a receiver, and the term "receiver" should be understood to mean the presence of a transmitter.

Throughout this disclosure, the term “detection network” refers to a wireless network used in the systems and methods of the present disclosure to detect the presence of biological masses intervening within the communication area of the network. The detection network can use general networking protocols and standards, and is not necessarily a specific purpose network. That is, the nodes of the network may be deployed for a specific purpose to set up a wireless detection network in accordance with the present invention, although this is not necessary and will generally not be the case. A normal wireless network established for other purposes can be used to implement the systems and methods described in this application. In a preferred embodiment, the detection network uses multiple Bluetooth (TM) low energy nodes, but the present disclosure is not limited to such nodes. Each node acts as a computer with appropriate transmitters and receivers to communicate over the network. Each computer provides a unique identifier within the network so that each time it sends a message, the receiving computer can identify from where the message originated. This message origination information will be important to the functionality of the present invention as generally described in the detailed description. The receiving computer then analyzes the incoming signal properties, including signal strength, bit error rate and message delay. The detection network may be a mesh network, which means a network topology in which each node relays data from the network.

 Throughout this disclosure, the term "node" means a network communication, typically a start point or end point of a device that has a wireless transceiver and is part of the detection network. Nodes are generally standalone self-contained networking devices such as wireless routers, wireless access points, short-range beacons, and the like. The node may be a general purpose device or a special purpose device configured for use in a detection network as described in this application. By way of example, and not limitation, a node is a device having a wireless transmission function of an off-the-shelf wireless networking device by adding specialized hardware, circuitry, components, or programming for implementing the systems and methods described in this application. Can; That is, it detects significant changes to signal properties, including but not limited to signal strength, bit error rate and message delay. Within the detection network, each node can act as a transmitter for signals to the network, as well as receivers for other nodes to push information. In a preferred embodiment, the node uses Bluetooth Low Energy (BLE) as a wireless networking system.

Throughout this disclosure, the term “continuous” refers to the occurrence of an event on an ongoing basis over time, whether mathematically continuous or discontinuous. The generally accepted mathematical definition of "continuous function" describes a function without holes or jumps, which is generally described by a two-sided limit. The techniques described in this application are based on disturbances to the telecommunication system, where the transceiver transmits at discrete intervals, and the received raw data is taken discretely, ie at discrete time intervals. The resulting data itself can be discrete in that it captures the characteristics of the system during a particular observation window (ie, time interval). In a physical or mathematical sense, this mechanism is essentially a set of discrete data points implying discontinuous function. However, in the context of the art, a person skilled in the art will understand that a system exhibiting this type of behavior is "continuous" if this measurement is taken on an on-going basis over time.

The measurable energy density signature of the RF signal is influenced by environmental absorbers and reflectors. Many biological masses, such as the human body, are mostly water and serve as important energy absorbers. Other properties of the human body, such as clothing, jewelry, and internal organs, all affect additional measurable RF energy density. This is particularly true if the RF communication device is transmitting over relatively short distances (eg, less than 50 meters), such as Bluetooth, WiFi, 802.15.4 (Zigbee, Thread) and Z-Wave transceivers. The human body passing through the physical space of the network can cause signal absorption and interruption. Due to the relative uniformity of size, density and mass composition, the human body can cause characteristic signal absorption, scattering and measurable reflection. Changes in signal behavior and / or properties are generally referred to as "Artifacts" in this application. This phenomenon is particularly useful in the ISM (Industrial, Scientific, and Medical) band of the RF spectrum, but can generally be observed in bands beyond the range.

 In an RF communication system comprising a transmitter and a receiver separated in space, the signal received by the receiver from a given transmitter consists of energy from the original transmitted message arriving at the receiver. In general, objects in the transmission path affect the characteristics of the final signal arriving at the receiver.

 Communication systems are generally designed to handle these issues and also faithfully reproduce messages from the transmitter. Since the human body is generally present, as long as RF communication is affected, as a large amount of water, one of the observable differences between the presence and absence of the human body in the detection network is signal absorption by the human body. In general, the closer to the transmitter or receiver, the more important absorption can be.

In general, the human body is expected to generate artifacts in the detection network in a somewhat predictable manner that can be detected or identified programmatically by a detection algorithm. In addition, artifacts can be cross correlated across the network to determine the estimated location of the object causing the artifact. The accuracy of this estimate may vary depending on the algorithm selected / configured and the equipment used in the individual system.

For each given algorithm that is selected / configured, the system can establish detection such as a combination of a baseline signal profile in which the human body is not present in the detection region and a baseline signal data in which the human body is present in the detection region. The new incoming sample baseline signal data is compared to known sample baseline signal data and baseline signal profile to determine the presence or absence of a human body in space.

Short-range low-power communication networks typically operate using signals in the 2.4 GHz frequency band, which is notable for being noticeable within the energy frequencies observed by the human body to absorb. As indicated, the human body physically intervened in the detection network absorbs and / or reflects at least some of the signals transmitted between and among the nodes. However, other effects such as forward and backward scattering may also occur. Utilizing data collection in the detection network without the presence of a human body to establish a baseline, and future elements of the data for statistically significant differences typically represented by the physical presence of one or more human bodies regardless of whether one or more human bodies are moving By investigating, the detection network makes decisions regarding the presence or absence of a human body within the network.

Depending on the communication network itself, the hardware used, and the human body, these changes can be registered within the network in different ways to produce different results; However, such changes are detectable. This differs from radar technology in that the detection of an object relies solely on signal reflection or not, but often on signal absorption, the opposite principle detected through measurable changes in signal characteristics between the transmitter and receiver at different physical locations. .

 By analyzing changes in signal characteristics between nodes in the network, the disruptor and, for example, the position of the human body can be calculated relative to the network. Since a simple existence of the body is sufficient, the system does not necessarily include the origin element, and does not need to rely on motion or movement. Since the origin element is not required, the systems and methods described in this application can provide an anonymous location data reporting service, with respect to traffic, travel routes and occupancy without the need for additional components or devices to be associated with the body being tracked. Data can be collected. Generally speaking, the systems and methods described in this application operate in real time.

1 is a schematic diagram of a system and method according to the present disclosure. In the illustrated embodiment 101 of FIG. 1, a detection network 103 comprising a plurality of nodes 107 is disposed within a physical space 102 such as a room, corridor, hallway or doorway. In the illustrated embodiment of FIG. 1, an indoor space 102 is used, but the systems and methods described in this application are also operable in an external environment. In the illustrated embodiment, node 107A is communicatively coupled 111 to a telecommunication network 115 such as an intranet, the Internet, or the Internet (111). The server computer 109 can also be communicatively coupled to the telecommunications network 115 and to the node 107A connected thereby (113). The illustrated server 109 includes programming instructions for implementing the system described in this application and performing the method steps described in this application. However, in one embodiment, the functions performed by the server may be performed by one or more nodes 107 with appropriate software / programming instructions or modified as appropriate.

In the illustrated embodiment of FIG. 1, each node 107 is communicatively connected to at least one other node 107 in the detection network 103, and two or more or all other nodes in the detection network 103 It can be communicatively connected to (107). For example, in a typical wireless network deployment strategy, multiple wireless access points are deployed throughout the physical space 102 to generally ensure that high-quality signals are available everywhere. These nodes 107 collectively form a detection network 103, and can transmit data to each other or only to a router or set of routers. In the illustrated embodiment of FIG. 1, node 107A is a wireless router and other nodes 107B, 107C and 107D are wireless access points. However, this is one possible configuration. Also, any given node 107 need not be a particular type of wireless device. Any number of nodes 107 may include routers, access points, beacons, or other types of wireless transceivers. In addition, any number of nodes 107 may be present in one embodiment, but at least two are preferred. More nodes 107 in space 102 increase the amount of data collected (as described elsewhere in this application), and improve the likelihood that the human body is generally interposed between at least two nodes 107 To improve location resolution.

In the normal course of operation, the node 107 frequently transmits and receives radio transmissions. For example, when the wireless router 107A receives a data packet, the wireless router 107A typically broadcasts a wireless transmission that includes the packet. This means that any receiver within the broadcast radius of the router 107A can receive the signal regardless of whether it is intended or not. Likewise, when an access point receives local data, such data is also broadcasted and can be detected by other access points and routers. Even if user data is not actively transmitted in the network, other data is frequently transmitted. Such other transmissions may include status data, service scans, and data exchange for the functions of the lower level layers of the network stack.

Thus, each node 107 in a typical detection network 103 receives a transmission on a consistent basis, and in a busy network, this may in fact be a continuous basis. Thus, the detection network 103 can be used to calculate the presence and / or location of a biological mass 104 or 105 physically intervened within the transmission range of the network 103. Since the presence of the human body affects the nature of the signal being transmitted between or among the nodes 107 in the network 103, such presence can be detected by monitoring changes in such properties. This detection can also be performed while the data of the data packet being transmitted and received is still being transmitted and received; That is, detection occurs in normal data exchange between two or more nodes, or among two or more nodes, which continues regardless of detection. Specifically, the wireless network operates to transmit data between nodes, while simultaneously detecting data and locating the object using characteristics of a manner in which data packets consolidating the data are affected by the presence of the object in the transmission path. can do.

In the illustrated embodiment of FIG. 1, at least one node 107 is itself in response to a statistically significant change in signal characteristics even while waiting, receiving and / or transmitting communication between itself and another node 107. The communication signature between 107 and at least one other node 107 is monitored. The specific geometry of the physical space 102, including the presence and location of fixtures in the physical environment, generally does not affect the system, wherein monitoring is based on statistically significant changes in signal properties that indicate or demonstrate human characteristics. Because it is about. That is, the change in signal characteristics is due to a change in the absorber or reflector, such as the human body, in a physical environment or communication space covered by the detection network 103. Detection of the presence of a human body within the detection network 103 can be performed using a statistical analysis method for signals, such as using a sensing algorithm, as described elsewhere in this application. Again, this does not require the human body to be associated with an origin element or motion. Instead, the detection network 103 is because a new object (usually a human body object) has been introduced into the communication space and the presence of that object has changed the network communication between the nodes 107, typically the characteristics of the data packet. It detects that the characteristics of network communication have changed.

To detect the change, a baseline of signal characteristics is developed which generally compares the recently transmitted signal. These characteristics are derived from typical wireless communication network diagnostic information. This baseline of signal characteristics between nodes 107 is generally established prior to using detection network 103 as a detector. This can be done by operating a detection network 103 in which a typical or normal environment, i.e., detection network 103 communicates data packets, and does not have a significant biological mass intervening in the physical broadcast space of detection network 103. . For a period of time during such an operation, signal characteristics between the nodes 107 and / or among the nodes are monitored and collected and stored in a database. In one embodiment, server 109 receives and stores such data, but in one embodiment, one or more nodes 107 may include hardware systems configured to receive and / or store such data.

For example, if node 107 includes special purpose hardware and programming for use in accordance with the present disclosure, such node 107 may store its own signal characteristic data. Such signal characteristic data may be data related to the received energy characteristic of a signal received by a specific node 107 from one or more other nodes 107. Baseline data establishes a signature characteristic profile for each node 107, which is typical of the signal received by node 107 under normal operating environment essentially free of significant biological masses intervening in detection network 103. And / or a set of data defining general characteristics. Node 107 may have one or more of these profiles for different nodes 107 receiving data.

In one embodiment, after baseline signatures are detected and collected, the detection network 103 will generally continue to operate in the same or similar manner, but can now detect the presence of biological mass. This is usually done by detecting and collecting additional signal characteristics in real time when the detection network 103 is operating in normal mode, sending and receiving data packets. These newly generated real-time signal characteristic profiles are also generally the characteristics of the signal between two specific nodes 107 in the detection network 103, so that the corresponding baseline signal characteristic profiles for the same two specific nodes 107 and Can be compared. Statistically significant differences in specific properties between the two profiles can be interpreted as being caused by the presence of significant biological masses such as the human body.

The comparison operation can be performed by appropriate hardware at a given node 107, or a real-time signal characteristic profile can be sent to server 109 for processing and comparison. In a further embodiment, both are performed so that a copy of the real-time data can also be stored and accessed through the server, effectively providing a history of the signal property profile.

This means that as described in this application, at least some signal properties between at least two nodes are altered when a data packet is transmitted in which a biological mass interposed within the network generally intercepts and / or interacts with the biological mass. Because it does. The extent and nature of the change will generally relate to the nature (eg, size, shape and composition) of the particular biological mass involved and its location 103 in the network. For example, when a housefly flies through the detection network 103, the amount of signal change may be small enough to be indistinguishable from natural variations in signal characteristics. However, the larger the mass, such as the human body, the more substantial and statistically significant changes in signal properties may occur.

This change is not necessarily revealed in all signal characteristic profiles for the detection network 103. For example, if a mass is interspersed at the edge of the detection network 103, the node 107 closest to that edge is likely to experience a statistically significant change in signal characteristics, while on the other side of the detection network 103 Nodes will have little or no statistically significant changes (with signals from each other passing through or not around the biological mass). Thus, if the physical location of the node 107 is also known, the system not only determines whether the biological mass is present in the detection network 103, but also which node 107 is experiencing the change and calculating the magnitude of the change. By deciding, you can calculate an estimate of where it is located.

This can be seen in the illustrated embodiment of FIG. 1. In FIG. 1, assuming that the presence of only one human body (A 104 or B 105) exists for simplicity, A 104 is typically a node 107C rather than between nodes 107A and 107C. ) And node 107A will have a greater effect on the signal characteristics. Moreover, A 104 also generally has a small bidirectional effect on the signal characteristics between nodes 107B and 107D. Conversely, B 105 will have a bidirectional effect on the signal characteristics between nodes 107A and 107C, as well as the signal characteristics between nodes 107B and 107D.

Although all nodes can communicate with each other, the effects of A 104 and B 105 are generally more pronounced in communications where A 104 and / or B 105 are generally not aligned with the communication path between the nodes. Can be ignored. For example, since the human body 104 or 105 is not in the transmission path between the corresponding nodes, no one of the human body 104 or 105 may seriously affect the transmission between the node 107A and the node 107B. However, A 104 can affect the transmission between nodes 107C and 107D.

It should be noted that the presence or absence of a biological mass within the communication area of the detection network 103 will not necessarily cause any changes in data communication. The detection network 103 is expected to use its standard existing protocols, means and methods (including all types of retransmissions and error checks) to ensure that the data in the transmitted data packet is correctly received, processed and operated. Indeed, the detection process of detection network 103 is performed in addition to the standard data communication of detection network.

It should be appreciated from this that the data in the data packets communicated by the node 107 in the detection network 103 will generally not be used directly to detect biological mass within the communication area of the detection network 103. Instead, the data will simply be data communicated through the detection network 103 for any reason, and will often not be related to the detection of a biological mass. Moreover, the present disclosure generally contemplates packetized communication in the form of data packets, but in alternative embodiments, data may be communicated continuously in an unpackaged form.

In one embodiment, to enable the detection network 103 to detect the presence or absence of a particular biological mass, the system includes training aspects or steps. This aspect may include, after the baseline is established, one or more human bodies are intentionally intervened in the network at one or more locations in the network, and one or more additional baseline data sets are collected and stored. This second baseline can be used for comparison purposes to improve the accuracy of detecting the size, shape and / or other properties of biological masses intervening in the network and / or improve the accuracy of positioning. Such training may use supervised or unsupervised learning and / or may use techniques known to those skilled in the art of machine learning.

In one embodiment, the detection network 103 can use a specialized protocol that includes a controlled messaging structure and / or format that can be controlled from one node 107 to another, such that any node 107 It is simpler and easier to determine if a message originated from) and allows control of aspects such as the composition of the transmitted signal, the transmitted signal strength and the duration of the signal. This control further facilitates certain improvements in processing and identifies and uses specific signal qualities and / or characteristics specific to the detection aspects of the network 103 that may be different from the common networking aspects sharing the same network 103. Enable the receiver. When controlling messages sent and received on the opposite side of the positioned mass, there is no need to send a signal as a scan or sweep an area in space, such functions are much more than is necessary for a typical broadcast or directional transmission between nodes 107. This is because they tend to require more expensive equipment. The message is generally constructed in such a way that it produces the best data available for detection algorithms that are configured to work best in the communication network in use. In general, such a configuration still avoids the need for waveform level analysis of the signal transmitted by the network.

In the illustrated embodiment, each node 107 can generally determine the originating node 107 of a packet received by such node 107. This message origination information is typically encoded within the message itself, as is known to those skilled in the communication network. As a non-limiting example, this can be done by examining the data embedded in the protocol established in the networking stack, or by examining the data sent by the originating node 107 for specific purposes to implement the systems and methods described in this application Can be performed. Typically, each node 107 has the appropriate hardware and processing power to parse the received message. Although many different topologies and messaging protocols allow the functionality described in this application, mesh networking topologies and communication methods in general will produce useful results.

2 shows an embodiment 201 of a method according to the present disclosure and should be understood in relation to the system of FIG. 1. In the illustrated embodiment, the method begins (203) with the establishment of a detection network (103) comprising a plurality of communication nodes (107) in accordance with the present disclosure. As is known to those skilled in the art of establishing a communication system, there are many different approaches to the setup of such a network 103, and within this framework many different network 103 topologies may be feasible.

Next, a digital map of memory representing the physical node 107 geometry of the detection network 103 can be generated (205). The detection algorithms described in this application generally use information regarding where nodes 107 are located in physical environment 102. Data regarding this physical location of the node 107 may be manually supplied to an accurate diagram of the physical network environment 102 and / or software may map a relational location map of one or more nodes 107 within the detection network 103. Used to automatically generate, facilitate the placement of node 107 on such an environment map or diagram.

Alternatively, the node 107 can be placed on an empty map or an empty map or diagram using a relational (opposite absolute) distance for detection. In this dimensionless system, a message can still be generated from an algorithm related to the detection of the human body in the system (101), and the user regarding the transmission of the human body movement and / or presence message within the network 103 Additional manual processing such as input may be included.

In embodiments with automatic node 107 location detection, node 107 location is, for example, but not limited to: the physical location of a particular hardware component, such as node 107 and each for one or more other nodes 107 Setup and configuration of the detection network 103 including the location of the node 107 of the network; Signal strength indicator; And algorithmically and / or programmatically detected by one or more nodes 107 and / or computer servers 109 based on factors such as transmission delay. In the illustrated embodiment, this step 205 includes overlaying the map generated on a digital map of the physical space 102 or environment (referred to herein as an "environment map"). , The detection network 103 occupies the floor plan of the building. This step 205 additionally and optionally includes a scaling element for aligning the scale of the generated map to the environment map, and a user manipulation and / or modifiable input element for adjusting the generated map for fine adjustment. Therefore, as will be understood by those skilled in the art, it more closely conforms to the actual node 107 deployment geometry. In an alternative embodiment, each node 107 can be manually placed at an appropriate location on the environment map without using a relative location algorithm.

Either way, this step 205 establishes the physical location of the node 107 in the detection network 103, which allows determination of the location of the intervening biological mass due to the presence of the human body within the detection network 103. Will do. By placing the node 107 on the map (either manually or through an automatic means), the node 107 is a human body in the network 103 based on how the baseline signal affects communication between the various nodes 107. Can trace the existence of The system 101 then uses the collected information about the signal reaching the receiver, given a set of transmitted information known to the data processing algorithm. The data processing algorithm ultimately determines whether the human body is within the network 103 and / or where the human body is located within the network 103.

Next, the messages are constructed and exchanged according to the protocol in a format determined to be suitable for detecting the presence of a biological mass within the network 103 (207). This can be done using general purpose networking protocols known in the art, such as protocols in the OSI network model, or special purpose protocols that replace or complement these general purpose protocols.

In general, this step further comprises controlling and / or modifying (207) the messages passed within the detection network 103 for specific purposes to detect human presence and enable simplified statistical analysis. Do. By controlling message exchange (207), system 101 coordinates for common content sent over detection network 103, while not limited, transmission intervals; Transmit power; Message length and / or content; And it may enable adjustment of parameters including the intended message recipient (s). Again, the system does not need to rely on waveform level analysis, so it can operate within the scope of wireless communication standards.

Controlling these parameters 207 enables the development of statistics and / or analysis, which may be based at least in part on predefined or expected message content or characteristics. Such content and / or characteristics may include, but are not limited to, transmission time stamps and / or transmission power levels. By controlling and modifying these aspects, the detection network 103 upon initiation of presence, such as, but not limited to, automatic gain control (AGC) circuitry that may be incorporated into specific receiver hardware at node 107. Hardware constraints, including hardware features that cause unwanted results when used in can be overcome.

In the example 201 shown next, the space 102 clears (209) a significant biological mass, particularly the human body 205. Then, a statistical baseline of signal strength is developed locally by each node 107 (211). Again, by placing the node 107 in the map via manual and / or automatic means in step 205, the node 107 is based on how the baseline signal affects communication between the nodes 107. The presence of the human body can be tracked in the network 103.

Next, the biological mass enters the detection network 103 (213), causing signal absorption and other distortions, which manifests in changes in signal properties between the nodes 107. These changes are detected 215 and analyzed 217 to determine if the changes indicate the presence of a human body or other type of biological mass that detection network 103 is configured to detect. This detection is more limited to the area between the nodes, such as within at least the inner area between the three nodes on the network, but is possible with greater accuracy depending on the algorithm and hardware in use at the time.

Generally, this is done using detection algorithms executed by one or more nodes 107 or server computers 109. Node 107 and / or server 109 use software to determine the location of the detected biological mass in detection network 103 using one or more detection algorithms. These algorithms generally compare the baseline profile to newly detected signals, and also for example without limitation: the physical location of a particular hardware component, such as node 107, and each node 107 for one or more other nodes 107. Setup and configuration of the detection network 103 including the location of); Signal strength indicator; And various data and other aspects such as transmission delay.

Generally speaking, as described elsewhere in this application, these algorithms compare the newly collected signal characteristic profile 215 to the baseline signal characteristic profile 211 to identify the change and make the change based on the nature of the change. And determining whether it represents the presence of the human body. This determination can be made at least in part using training data developed through machine learning as described elsewhere in this application.

In one embodiment, the detection algorithm may further include the use of the observed signal characteristic change (s) between one or more pairs of nodes 107 in the detection network 103, correlated in time and relative effects. These factors enable identification of the physical location in the detection network 103 where such a signal change has occurred, allowing estimation of the physical location of the human body that causes this signal characteristic change (s), which in turn results in a biological mass. It can be used to estimate the physical location in the environment of the detection network 103 involved. These physical locations may be provided in simple x, y, z coordinates depending on the coordinate system, or may be visually displayed, for example, on a map.

When multiple human bodies are present in the detection network 103, it is more difficult to separate the effects of various individuals from each other, and generally accuracy will be improved by adding more nodes 107. In one embodiment, techniques such as advanced filtering and predictive path algorithms can be used to individually determine a person's location within the network 103. Although the movement of the human body in the network 103 is not required for the systems and methods to operate properly, the movement or lack of movement can be used to improve detection accuracy by predicting the path of a single individual. This can help an individual statistically identify a "disappeared" instance from the detection network 103, but the system has enough data to assume that the individual is still present on the network 103.

For example, if an individual's movement path is predicted and terminates next to another detected individual, the system 101 may determine that the two individuals are too close to identify the signal characteristic profile changes individually, but due to the movement of the individual Has not been determined that the individual is outside the network 103 detection range, and the algorithm determines that the individual is in close proximity to the other detected human body and does not move. Thus, when one of the two adjacent stationary human bodies moves, the algorithm can again identify each individually and resume route prediction based on observed signal characteristic profile changes.

In this way, the systems and methods according to the present disclosure can track whether one or more individual human bodies within the network 103 move, and whether those human bodies are associated with an origin element. In order to estimate who a particular individual is, specific personal identification may be further performed using different path prediction and sensing algorithms, such as those used in human body tracking technology in the robotics industry. It should be noted that the individual enables a specific individual to be identified, and can also have a specific and unique effect on various signal characteristics that allow one particular individual to be distinguished from other individuals. This effect can be used to further determine the location of a particular individual within the detection network.

The detection algorithm (s) are generally, but not limited to, configured to take advantage of the characteristics of the communication signal taking into account factors such as the frequency of the signal (s) and the transmitted power level of the signal (s). In one embodiment, the algorithm determines the effect of the presence of the human body on signal characteristics in an RF environment within a communication network, and then uses a data-driven method to identify when the effect is later observed. Presence is detected.

For example, in one embodiment, the signal characteristic that varies with the presence of the human body is the signal strength registered between nodes 107. This is especially the case in BLE networks, and statistics related to signal strength over time can indicate the presence of a human body within the network. Such artifacts can be used by the detection algorithm (s) to provide information about the physical location of the object causing the artifact. That is, by combining various statistics for artifacts captured through network 103, the system determines where artifacts are in physical space 102, and thus, where the human body is within network 103.

In the simplest use case, the algorithm can simply identify changes in signal characteristics similar to those known to be caused by the presence of the human body (e.g., from training), and whenever such changes are detected relative to baseline. The detection event 219 can be triggered. This may seem like adjustment of the average, standard deviation, skewness or variance of the signal strength depending on the system 101 used. When the detected signal characteristic profile returns to a profile similar to the baseline, it can be assumed that the physical environment 102 has returned to an empty state with respect to the presence of the human body.

To elaborate on the simplest use case, the baseline profile in this case consists of some or all of the baseline profiles that exist when no human body is included in the space, and may be subject to physical adjustments to that space. . Simpler algorithms that can account for changes related to newly detected human changes in relation to recent baselines can be used to deal with such situations; However, if the baseline has been changed, it is desirable for the system to accurately determine whether one or more current signal profiles match the empty baseline profile or a degree of occupancy profile. This determination can be made in response to movement, but is not made in response to movement, but rather is based on whether the characteristics of such a signal can be correlated with one or more empty baselines or one or more presence signal profiles.

Compared to other techniques (typically Passive Infrared (PIR) sensors) used in these determinations that require motion to perform a function, the systems and methods described in this application are based on the presence, movement, and presence of a static human body within space 102. It can detect whether or not, and more accurately, when the human body is no longer in the space 102. For applications such as security and occupancy sensing, this system can be more difficult to cheat. Some examples of tricks that can deceive PIR and other similar motion-based techniques are holding the seat while the human body enters the space and moving very slowly or staying stationary in the area after entering. Another advantage is that the system does not require additional hardware other than the hardware used for normal network communication. This is because additional software and processing functions can be provided through modification of external components or existing hardware, such as implementing appropriate software such as SOC (System On a Chip) attached to a ready-made communication module. If additional processing power is desired, additional processing node (s) can be added to analyze the signals propagated between the nodes 107, or a workload is sent to the dedicated server machine 109 and it is Can be processed by

Determining the presence and / or location of the human body may relate to the specification of a particular signal type to be analyzed, and to control the signal transmitted between nodes 107 on network 103 to best achieve such detection. Exemplary related to signal absorption, reflection, backscattering, etc. due to additional human body between nodes 107 by sending a controlled communication pulse through network 103 where the original signal is known and the transmitted power can be modulated. Develop data. In general, since the baseline system can be configured without the presence of the human body and these baselines are assumed to look statistically different from the presence of the human body, signal characteristic changes can be further assumed to be due to the presence of the human body in the network. By allowing the input of the timer when space 102 is empty and generally configuring the system to improve baseline definition, the system can be recalibrated periodically to achieve improved accuracy. Generally speaking, the tracking algorithm utilizes the best available triangulation calculation combined with statistical methods known to those skilled in the art of location technology, combined with a detection algorithm for detecting the human body within the network 103.

The present invention does not require an origin element associated with the detected human body, nor does the human body need to have any device capable of communicating with the network; However, such technology uses such elements when deployed within a system. Adding these elements can reduce the computational burden on the system and increase accuracy. The systems and methods described in this application do not exclude these additional features and can be improved thereby. Augmenting detection with an inference engine allows the sensing hardware to recover from false alarm conditions or other edge cases, making the system more robust. Such an inference engine may further supply information to the machine learning system, which may further modify one or more baseline signal profiles or one or more presence signal profiles to improve the performance of the system.

In one embodiment, the detection network 103 implementing the systems and methods described in this application may further include an element 219 to take action based on the presence and / or location of the detected human body. It determines, for example, the presence and / or location of the human body on the network through the network first, and then determines the action to be performed based on the presence and / or location of the human body on the network, and uses the network to take the action. It can be done by sending a control signal over a network using a computer to send a message through. Since the communication network and the network performing detection may be the same network, the invention described in this application extends the traditional functionality of the communication network to include human body detection and / or location sensing without requiring additional sensing hardware.

Computer elements of the network have to perform additional calculations and can generate communication signals. This can reduce the computational burden on the computer; However, the network can still function as a command and control network, independent of the network as a detection network.

The system ranges from occupant sensing, which can be used for lighting control and / or security as a whole, to counting the number of people in the space needed for heat and / or traffic maps to the system tracking individual human bodies moving through the space. It can be used for applications. The technology can be integrated into the network node itself, or it can be a combination of nodes that transmit information to a processing element (either directly on the network or in the cloud) to perform calculations to determine the desired information. The final integrated suite can be tailored to the application and can be used in a variety of ways.

No additional sensor is required (but in one embodiment, a sensor may be present), but detection is effectively accomplished by calculating statistics from a traditional RF communication stack. Such a system prevents the collection of personal data from people walking through the space, since this system knows that water, organs, clothing, etc. of a mass of about the size of a human body have passed, and requires a separate device that acts as a starting point element. Do not. As such, this technique is a significant departure from the traditional method of tracking the human body moving through space.

Logical extensions of the systems and methods described in this application include dynamically processing functional network messages within statistical analysis to avoid or reduce additional messaging overhead for the system. Further, in one embodiment, the systems and methods described in this application are extended to include dynamic adjustments to network and / or message structure, configuration and / or operational parameters based at least in part on functional messages transmitted within the network. Is considered to be

Moreover, since tracking is generally based on signals influenced by a human body mass, the system does not depend on the human body moving around for detection. Because it does not rely on motion, it can overcome many of the drawbacks associated with traditional presence sensing techniques such as passive infrared and ultrasonic sensing techniques.

The use of communication network signals between nodes in order to detect the presence of a human body in a network in which the human body does not have an origin element is rapidly deviated from the current non-fiducial element detection method and performs existence sensing in a completely new method. To use the communication network. The combination of detection techniques and the use of network nodes as a transmitter receiver combination to perform human body presence detection presented in the present application provides a new type of human body presence detection system that does not require additional equipment beyond that required to form the communication network itself. Make up.

The systems and methods described in this application can be implemented in a communication network without affecting the operation of the network itself for the purpose of normal communication. The network continues to operate as a communication network as its main function, but in this case some communication is used to calculate the location of the human body present in the network. Since the systems and methods described in this application utilize the basic operation of the network, a human body in the network that further carries a transceiver device known to the network can be detected and positioned with increased accuracy. Such transceiver devices, which may include mobile computing devices with wireless transceivers, such as, for example, mobile phones, cell phones, smart phones, tablet computers, wearable computer technology, etc., can be connected to a network and use traditional triangulation methods known to those skilled in the art. Can be located by using the network. Machine learning algorithms can be applied when a person carries such a transceiver, which in turn can further improve performance.

The calculation of the location of a known transceiver device can be compared to the location of a person determined by the non-transceiver aspects described in this application. If the communication network reports both the location of the origin elements and the human body within the network, these two locations can be compared. In general, since the detected position of the starting point element has a higher accuracy than the estimated position of the human body based only on the network communication, the position calculation of the position of the human body in the network calculates the position of the system for the next human body entering the network It can be tuned using machine learning algorithms to improve performance.

Using machine learning algorithms, the system can improve the accuracy of the location prediction algorithm based on the known location from the transceiver. This allows you to confirm your prior decisions and refine your future decisions. For example, if the previous decision turns out to be consistent by an approximately equal amount, that amount can be applied to future decisions as adjustments. In this way, the system can be continuously improved and trained to better locate the human body within the network. Likewise, machine learning can continuously improve detection and false alarm rates. By way of example and not limitation, data relating to previous traffic patterns at a facility may be used to establish defaults, estimates or expectations regarding a range of times or dates when a particular facility is generally occupied or generally empty. The system can use this data to improve performance.

Additionally, the system includes a network element; That is, based on the presence of a human body, such as a network-operable electrical switch, door, motion sensor, infrared sensor, etc., make inferences or make inferences based on physical interactions with devices or components that are attached to or communicate with an operable or operating network. It is configured to derive. This physical interaction can be regarded as an origin element at the time of interaction for the purpose of the system. As an example, if a light switch that is part of a network is activated, the system may assume that a human body was at or near the physical location of the switch when the switch was operated. As such, the system applies that information to the known data points that machine learning can apply to better predict the presence of the human body in the future (i.e., that the characteristic reflects the human body at a specific location near the switch). The signal characteristics of various network devices can be used as a check point). Additionally, these events can be used as presence triggers for other purposes, such as security alerts. As an example, assuming that the system is in secure mode and someone has found a way to hide its presence but still interacts with the switch, the system can determine if someone exists and send an alert based on the interaction with the switch. Generally speaking, interactions with systems are defined physically and logically, and logical interactions will include typical usage patterns based on time, external inputs, and so on. These systems serve as a backup for RF presence sensing and provide additional machine learning capabilities to the system.

Additionally, the system can estimate whether the mobile transceiver in the network is actually being carried by the human body, for example where the human body has left the device at a location in the network. Since the system can detect the human body as biomass through a change in signal characteristics, it is possible to detect whether the transceiver is present in the network while there is no human biomass. This prevents erroneous training of the system and prevents the baseline and presence signal profiles from being corrupted by data not correlated with these profiles.

In one embodiment, as an additional input to the inference engine, if some indication of the system changes the state, and the human body within the detection area behaves in a way to correct the system state, the system better reflects the user preferences It can be inferred that the baseline and prerequisite profiles to do so need to be adjusted. For example, if the lighting in the space needs to be turned off with the human body in the space, the human body may behave physically, such as physically moving or waving its arms, or simply looking for a wall switch or walking towards the wall switch. . This movement can be detected in a reasonable amount of time, and the system can determine that the space is erroneously determined to be empty and adjust the baseline and presence profile accordingly. These activities can be referred to as inferring the presence of one or more human bodies in space.

As a side effect of collecting various signal characteristics and executing them through various algorithms, the system can run multiple detection calculations simultaneously to achieve different performance criteria in the same system. By way of example, the same communication network can be used for detection related to lighting and security; However, the collected statistics can be processed differently for both applications, but can be processed simultaneously. In this way, the lighting application can still provide a shorter detection time to detect, but potentially a higher false alarm rate, while the security application can trade a slightly longer time to detect while reducing the false detection rate. have. The signal characteristics processed by the system may vary depending on the application, but all are captured in a communication network and can be processed simultaneously in multiple ways. This processing method can be encapsulated with several sets of different sample baseline signal data to determine detection relative to the baseline signal profile.

In one embodiment of the system according to the present invention, the system includes a communication system capable of determining the presence of one or more human bodies from information regarding wireless signals between two or more computers on a network, each computer having a transceiver for communication ; And a computing element for performing calculations, each computer sending a signal to one or more other computers on the network, the signal including a unique identifier of the computer sending the signal; Each computer processes the received signal for the purpose of determining the presence of one or more human bodies; And one or more human bodies need not have any device capable of communicating with the network.

In one embodiment of such a system, the algorithm uses statistical methods to determine the presence of one or more human bodies. In a further embodiment of such a system, the statistical method determines the number of human bodies present. In other further embodiments of such a system, the system may determine the physical location of one or more human bodies on the network. In another embodiment of such a system, the system can track the physical location of one or more human bodies over time. In another further embodiment of such a system, the system uses information about the presence of one or more human bodies to control devices on the network. In one embodiment, the network is a mesh network.

In one embodiment, the computer determines their relative physical location, and further determines the relative physical location of one or more human bodies on the network. In a further embodiment, statistical methods are applied to the measurement of signal strength to determine the presence of the human body. In a further embodiment, the transmitted signal is controlled to more easily detect the presence of the human body. In another embodiment, the power level of the transmitted signal is controlled to make the presence of the human body easier. In a further embodiment, the system functions as an occupancy sensing system. In a further embodiment, the occupancy sensing system controls the lighting system. In a further embodiment, the network for controlling the lighting system and the network used for occupancy sensing use the same communication technology and hardware. In a further embodiment, the communication technology employed by the computer is selected from the list of Bluetooth low energy, Wi-Fi, ZigBee, Thread and Z-Wave.

In a further embodiment, the system functions as a sensing system for secure applications. In a further embodiment, the security sensing system controls the security system. In a further embodiment, the network used to control the security system and the network used for security sensing use the same communication technology and hardware. In a further embodiment, the system functions as a human body detector for the robotic system. In a further embodiment, the robotic system has a computer that dynamically positions various elements of the robotic system relative to each other. In a further embodiment, the network for controlling the robotic system and the network for functioning as a human body detector for the system use the same communication technology and hardware.

In a further embodiment, the system functions as a sensing system for HVAC applications. In a further embodiment, the HVAC sensing system controls the HVAC system. In a further embodiment, the network for controlling the HVAC system and the network used for HVAC sensing use the same communication technology and hardware.

In another embodiment, the system uses machine learning to improve detection ability and a person having his origin element: (1) using a known location technique to determine the location of the origin element; (2) using the system described above to locate a person; (3) comparing the position calculated by the method of (1) of this paragraph with the method of (2) of this paragraph; (4) Train the system by adjusting the positioning method using machine learning algorithms to improve the system's position calculation ability.

In other embodiments, the system may infer the presence of a human body in the network based on human bodies interacting with one or more computers on the network in any way. In a further embodiment, the system can use the estimated presence of the human body as input for machine learning to improve detection capability.

In one embodiment of the system according to the present invention, the system includes a communication system capable of determining the presence of one or more human bodies stationary and moving from information regarding signals between two or more computers on a network, each computer communicating For transceivers; And a computing element for performing calculations, each computer sending a signal to one or more other computers on the network, the signal including a unique identifier of the computer sending the signal; Each computer processes the received signal for the purpose of determining the presence of one or more human bodies; One or more human bodies need not have any device capable of communicating with the network.

In one embodiment, the algorithm uses statistical methods to determine the presence of one or more human bodies. In other embodiments, statistical methods determine the number of human bodies present. In other embodiments, the system may determine the physical location of one or more human bodies on the network. In other embodiments, the system can track the physical location of one or more human bodies over time. In other embodiments, the system uses information about the presence of one or more human bodies to control devices on the network. In other embodiments, information about the presence of one or more human bodies is available to one or more systems that are not directly related to the determination of existence. In other embodiments, the system has the ability to perform self-optimization to achieve a given performance according to one or more preset criteria.

In a further embodiment, the communication protocol or network is generally defined by a standard committee including, but not limited to, protocols such as Bluetooth Low Energy, Wi-Fi, ZigBee, Thread and Z-Wave. In another embodiment, statistical methods are applied to the measurement of the received signal strength to determine the presence of the human body. In other embodiments, the transmitting and receiving devices on the network can be selected and operated by the system to facilitate human body detection. In other embodiments, the power level of the transmitted signal can be controlled to make the presence of the human body easier. In another embodiment, the system functions as an occupancy sensing system for the lighting system. In another embodiment, the occupancy sensing system controls the lighting system. In another embodiment, the network for controlling the lighting system and the network used for occupancy sensing use the same communication technology and hardware.

In other embodiments, the system functions as a sensing system for secure applications. In another embodiment, the security detection system controls the security system. In another embodiment, the network used to control the security system and the network used for security sensing use the same communication technology and hardware. In another embodiment, the system functions as an occupancy sensor for heating, exhaust and cooling (HVAC) systems. In another embodiment, the occupancy sensing system controls the HVAC system. In another embodiment, the network for controlling the HVAC system and the network used for occupancy sensing use the same communication technology and hardware.

In another embodiment, the system uses machine learning to improve detection ability and a person having his origin element: (1) using a known location technique to determine the location of the origin element; (2) using a system to locate people; (3) comparing the position calculated by (1) of this paragraph with (2) of this paragraph; (4) Train the system by adjusting the positioning method using machine learning algorithms to improve the system's position calculation ability.

In another embodiment, the system can infer the presence of a human body in the network based on human bodies interacting with one of the computers on the network in any way. The interaction can be a direct physical interaction or an indirect interaction in response to a change in state in the system (eg, waving the arm in response to the light being turned off). In other embodiments, the system may use the estimated presence of the human body as input for machine learning to improve detection capability.

In addition, a communication system capable of determining the presence of one or more human bodies stationary and moving in a detection network from information regarding signals between two or more computers on a network is described herein, each computer comprising: a transceiver for communication; And a computing element for performing calculations, each computer sending a signal to one or more other computers on the network, the signal including a unique identifier of the computer sending the signal; Each computer processes received signals to determine the presence of one or more human bodies in two or more ways to achieve different performance criteria required to function simultaneously for two or more purposes; One or more human bodies need not have any device capable of communicating with the network.

In one embodiment, the algorithm uses two or more statistical methods to determine the presence of one or more human bodies according to two or more sets of performance criteria. In other embodiments, the system has the ability to perform self-optimization to achieve two or more performance sets according to two or more preset criteria. In further embodiments, the communication protocol or network is generally defined by a standard committee including, but not limited to, protocols such as Bluetooth Low Energy, Wi-Fi, ZigBee, Thread and Z-Wave. In another embodiment, two or more statistical methods are applied to the measurement of received signal strength to determine the presence of a human body according to two or more sets of performance criteria. In another embodiment, the system uses machine learning to improve the detection ability of two or more methods for determining presence and to have a person having their own origin element: (1) using a known location technique to determine the location of the origin element. To use; (2) using a system to locate people; (3) comparing the position calculated by (1) of this paragraph with (2) of this paragraph; (4) Train the system by adjusting the positioning method using machine learning algorithms to improve the system's position calculation ability.

In one embodiment, the systems and methods described in this application include change detection. As a non-limiting example, change detection can use or use a rolling baseline approach. In this embodiment, a first baseline is established and compared to a second baseline, and any differences between the first and second baselines caused by the presence of a human body in the detection network will be recognized by the system. You can. This is an RF environment caused by the presence of a human body in a different location when receiving a set of radio signal characteristic data from one or more nodes in the detection network, and based on this data, when the first baseline is established compared to the second baseline It can be done by software program to detect the change of. These methods can be used when the system is first set up in place to establish a minimum performance level without requiring empty space at startup. Such a system with change detection can improve overtime to the state between change and presence, where a limited aspect of presence detection may exist in such a system.

A representative example of change detection is shown in FIG. 3A. In the illustrated embodiment of FIG. 3A, the RF environment of FIG. 1 is depicted with a human body 301 present in environment 103 at discrete location 303. As described elsewhere in this disclosure, the nature of wireless signal transmission between nodes 107A-107D is affected by the presence of human body 301. In this particular example, the transmission between node A 107A and node D 107D is affected by the presence of human body 301. Thus, when the baseline is established (211) as shown in the method of FIG. 2, the baseline represents the radio signal characteristics and the human body 301 is in a discrete location 303. When the human body 301 moves to the new location 305, as shown in FIG. 3A, the characteristics of the radio signal between the nodes 107A to 107D will change.

In this particular exemplary embodiment, there will be little interference between nodes A 107A and D 107D when the human body is in position 305. However, there will be greater interference between Node B 107B and C 107C because location 305 is placed between these two nodes. Thus, when a difference is detected (205), a change in the position of the human body can be detected, as shown in FIG.

This is less difficult to implement than presence sensing because the human body does not exist and the need to establish a baseline in the detection network 103 is reduced. Such a system can primarily detect changes based on when the human body changes position and within the detection network 103 and update the operational baseline profile with a rolling basis. That is, in this embodiment, the baseline 211 is updated to be the same as the baseline when the human body 301 is in position 305. Thus, when the human body 301 moves to the third location 307, the detected difference 215 is the second baseline taken at the location 305 and the detection network 103 when the human body is at the location 307. Same as between radio signal characteristics. Similarly, the baseline 211 is updated to be the same as the baseline of the location 307, which can then be used to detect further changes in the location of the human body 201. This detection method uses a change in the radio signal baseline caused by a change in the position of the human body 301 in the detection network 103. This system has many advantages over conventional motion detection techniques such as passive infrared in that the system is not obscured by objects and can detect slow or gradual changes in position, which can be overlooked by conventional systems. Have In embodiments using this method, the baseline may be continuously updated.

In other embodiments, the system or method includes determining reliability. This aspect can determine the confidence that the third baseline corresponds to the first or second set of baselines. Reliability determination can use any number of techniques, including techniques known in the art, such as supervised training or the use of statistical methods to determine the degree of similarity or difference between data sets. Reliability may increase or decrease over time and may automatically make decisions regarding baseline differences with minimal differences, but still indicate the presence of a human body in detection network 103. The reliability of the determination of the presence or absence of a human body in the detection network 103 may be determined based on how similar the third baseline is to the first or second baseline. For example, if the third baseline is known to indicate the presence of a mass that affects signal properties, comparing the first baseline or the second baseline with the third baseline will identify the mass identified in the third baseline. The level of confidence that it is identical to the mass identified can be improved (or reduced). Depending on the reliability, the system can be configured to use different reliability thresholds in different operating contexts (eg HVAC, security, lighting, safety, etc.). A system or method involving reliability determination may operate across multiple systems using a common communication system, and the systems may include completely different nodes communicating with each other. A given node operates on multiple detection networks 103, enabling better system scaling when deploying systems and methods to multiple adjacent detection networks 103.

In other embodiments, baseline differences may be used to count or estimate the number of human bodies present in the detection network. In one embodiment, this can be done by estimating the amount of human body mass in the detection network and dividing it by the average mass per person. This can be done by establishing a first, empty baseline when there is no human body in the detection network 103, and establishing a second, occupied baseline when a known number of human bodies are present in the detection network 103. Next, a third baseline is taken and compared to the first empty baseline and the second occupied baseline. Then, the system software interprets the location where the third baseline radio signal characteristic fits the spectrum of the profile between the first empty baseline and the second occupied baseline, and totals in the detection network 103 from the decision. Estimate human body mass. This estimate can be based on the total mass of the human body in the detection network when the second occupied baseline is established.

As a non-limiting example, if the signal distortion at the third baseline is reasonable compared to the first empty baseline, the system can estimate that the amount of human body mass present is relatively low. However, if the amount of signal distortion is closer to that shown in the second occupied baseline, the system determines that the amount of estimated human mass present in the detection network 103 is closer to the amount that was present when the second baseline was taken. You can. Similarly, if the amount of distortion is determined to be much more extreme than that reflected in the second baseline, the system will exceed the amount of total human mass present when the third baseline is taken when the second baseline is taken. You can decide. Estimation of human body mass can be broadly based on the algorithms and methods described in this application, and can generally be adjusted to estimate multiple human bodies in space, as described above.

In another embodiment, the system uses entrance and exit signatures in the network diagnostic information to estimate the number of humans present in the space based on such signatures.

In this method, the entrance profile is established by the human body entering the space, the exit profile is set by the human body leaving the same space, and other profiles captured later are compared to the entrance and exit profiles to see if the human body has entered or exited the space. Decide whether or not. Inlet and outlet profiles are learned through normal system operation based on estimates from presence detection techniques and decisions based on state changes. By way of example, and not limitation, when the system detects a change and the presence goes from being undetected to being detected, such an event can be categorized as an entrance. Similarly, as a non-limiting example, if the system detects a change and determines that space has gone from occupied to unoccupied, such an event can be classified as an exit. The difference between the inlet count and the outlet count can be used to estimate the number of human bodies present in the space.

In another embodiment, the system uses inlet and outlet signatures in network diagnostic information in combination with estimates of the number of people derived by comparing the presence profile and sample profile of various person counts.

Each of these methods can be used with one or more counting methods to improve accuracy.

In one embodiment, the number or estimation of the human body present in detection network 103 is not necessarily limited, but may be used to operate other systems, such as HVAC systems.

In one embodiment, the position or position of the human body in the detection network is estimated. It estimates the range between various devices to determine the location of the human body, examines a subset detection area consisting of a larger number of nodes, uses various location baselines, and additionally determines the speed and speed of the human body in the detection network. This can be done by further extending the location system's ability to analyze position over time to estimate direction. In one embodiment of this method, the system may use a variety of node pairs, estimate the position of the human body between the pairs using nested estimates within the node pair based on baseline information, and then determine the actual position of the human body. To determine, the highest probability position for the human body can be determined in the detection network based on the overlap estimate.

In other embodiments, systems with a larger number of nodes may use more subset detection areas, typically systems with three or more nodes each, to determine the presence or absence of a human body in each space, The common occupied space can be estimated based on the overlapping occupied area, which can be assumed to be the most specific position of the human body in the detection network. As a non-limiting example, a set of four nodes may be subdivided into four sets of three nodes, where a location may be determined based on where a subset of the three node presences are detected. This sub-region creation allows detection within sub-regions where these sub-shapes are defined by overlapping regions created with a set of three or more nodes. Alternatively, multiple baselines can be established for a human body at different locations within the detection network, and subsequent baselines are compared to the baseline to determine a location within the human body's detection network. As a non-limiting example, a detection profile can be generated for a variety of locations within a detection region where a given detection profile corresponds to a human body at a given location in the network, and the sample profile is compared to a set of detection profiles corresponding to different locations, The system determines which detection profile is most correlated with the sample profile, and the system determines the position of the human body based on the location of the detection profile considered to be most similar to the sample profile.

Further, based on the detected change in the position of the human body in the detection network over time, the movement speed and direction of the human body in the detection network may be estimated. This can be done, for example, using interpolation and dead reckoning (or direct reconnaissance.) An exemplary embodiment of such a system and method is shown in Figure 3B. In the illustrated embodiment, The human body is located in the detection network 103 at time 0 at position 401. At a subsequent point in time 1, the human body is detected at another position (403). Since 403) is known, the distance 1 between them can be calculated. Additionally, the time from time 0 to time 1 can be determined or known. Assuming the distance is equal to the speed times the time, position 401 The movement speed of the human body from to to position 403 can be determined In addition, a vector representing the movement of the human body can be determined by implementing both direction and size (velocity).

However, if there are only two sample points, the probability of increasing the error rate increases and two or more sample points are needed. For example, in the illustrated embodiment, the third profile taken at Time2 places the human body at location 405. Again, the distance 2 from position 403 to position 405 can be determined, and the ratio of velocity between these positions can also be determined. In the illustrated embodiment, these positions are generally linear, suggesting that the human body is moving almost linearly in a given direction defined by the vector. Accordingly, the system can further estimate the future or expected location of the human body based on this data. That is, at time 3, the estimated position 407 of the human body may be determined based on the previously detected position. This estimation can place the human body outside of the detection network 103 and can further be used in the detection network 103 or to estimate the arrival or departure of the human body. Additionally, this information could potentially be used to alert other detection networks, or other segments of the detection network, that a person will soon arrive. This can be done, for example, by communication using computer server 109 over network 115.

Continuing the above exemplary embodiment, in the illustrated embodiment, the detected position change of the human body from time 0 to time 1 is 0.8 meters per second at a rate of 0.8 meters per second. The change detected in the distance from time 1 to time 2 is an average rate of 1 meter per second with an additional elapsed second of 1 second being 1.2 meters or a total of 2.0 meters for the two total elapsed seconds. Thus, at time 3 after 1 second, an additional 1 meter shift can be expected, and the estimated future location 407 is 1 meter further along the vector than the location 405.

In one embodiment, the systems and methods further train the system over time using machine learning. By way of non-limiting example, the system can be configured from one or more of the change detection techniques described above using, for example, but not limited to, known feedback and / or combinations of feedback from third-party systems, such as interactions with other smart devices. Data can be accumulated (thermostats, speech recognition systems, etc.) and / or inferences can be used over time to improve behavior according to expected or expected system behavior. Such inference can be based on normal behavior in space, direct human interaction with elements of the system, or changes in the sample profile from the body's reaction to system determination. As a non-limiting example, in a system implementing change detection to operate a lighting or HVAC system, user feedback as supervised training data indicating whether a given motion is correct (i.e., whether a change has been made in the lighting or HVAC system). System.

Similarly, in a system that implements presence detection, the user is forced to trigger change detection while in the detection network to establish an automated baseline execution for the occupied space based on the time the change trigger event occurs in the system. Means can be provided, allowing a time away from the change triggering event to be generally detected as empty. When combined with inference occupancy based on room type, this method can facilitate the system training itself, improving the function over time from change detection to presence detection level function. Effectively, by using a system based on change detection, the system can deduce the presence and absence, allowing to establish a baseline profile for when the human body is not present and a detection profile based on when the human body is present. In this way, the system will be able to train itself to move from operating as a change detection system to operating as a real presence detection system.

In embodiments that implement counting, the combination of change detection and presence detection can determine the estimated counting of the human body within the detection network, to estimate such a counting baseline profile and improve it over time. Such a system can facilitate the system training itself over time to count the number of human bodies in the detection network.

In embodiments that implement locating a person, the combination of change detection, presence detection, and human counting determines the location estimate based on overlapping areas and occupancy counts, and ultimately establishes a more accurate baseline estimate for time Over time, it is possible to improve the system's ability to locate the human body within the detection network. Such a system may include an inference engine, such as computer software running on a server, and / or may build estimates of expected system behavior from normal operation, and adjust operating parameters according to expected behavior. For example, if the detection area is typically empty from 10 AM to 3 PM, and occupied from 3 PM to 6 PM, the parameter expects an empty state from 10 AM to 3 PM, and 3 PM It can be adjusted to predict the state of existence from 7:00 pm to 6:00 pm). These inferences can be developed over time and improve performance at those times while maintaining overall flexibility.

Nodes can be placed in various location combinations to improve system operation. The system can operate with walls, ceilings, fixed nodes, mobile nodes and / or nodes located in a mixed configuration. Since the space is three-dimensional, the detection area can be defined by nodes on different floors of the building. Nodes can be placed on the wall in locations such as switches and exits. The broadcast range generally defines the perimeter of the detection area, and the system can be configured to examine the network diagnostics assuming that the human body is within the perimeter.

In one embodiment, one or more nodes may be placed on the ceiling. As a non-limiting example, this can occur when a node is integrated into a fixture and / or lighting system. In this embodiment, the node can generally radiate down into the detection area, and the system can be configured to examine network diagnostics based on radiation and multipath patterns different from those seen in switch and exit based systems. In this embodiment, the nodes generate communication in a generally downward direction, such that reflections from walls, objects, and floors generally ensure that RF energy reaches other nodes through multiple paths. Multipath also generally provides coverage of the detection area. This coverage due to multi-path means that the ceiling-mounted node behaves similarly to the wall-mounted node with respect to the impact of the human body on network diagnostic information.

Other fixed nodes, such as televisions; Monitors and smart home hubs are also considered, but not limited to. These nodes can be installed in walls, ceilings or fixed locations. Other nodes such as smart phones, tablets and laptop computers can be used as mobile nodes in the detection network. However, in this embodiment, the mobile node can first find its own position with respect to the fixed node in the system. If the location is set in the network, the accuracy of the system can be further improved.

In one embodiment, a combination of nodes can be used in the detection area. When combining a larger number of nodes, the system can determine the optimal node for operation. The optimal node can be determined by, among other things, determining the most efficient node for the selected functional level. As the number of nodes increases, the accuracy of the decision generally increases as well as the functional level.

In one embodiment, one or more nodes may operate in multiple detection regions. This enables improved system scaling, especially for adjacent detection areas. This scaling can also cause inference within a larger node network that includes a plurality of detection areas, and further facilitates tracking of human detection from one detection area to the next. For example, nodes may be shared between detection regions. A given node in the first detection network may have network diagnostic information based on communication within the first detection area, may also be part of the second detection network and may have network diagnostic information based on communication within the second detection network. The system can make independent decisions about how to operate the third party system in each of the two detection areas. Examining the reasoning for the detection region can improve the determination of the presence or absence of a human body within the detection region, particularly where a person leaves one detection region and enters another detection region. The detected change in signal characteristics can be used to determine the presence or absence of a human body in individual areas based on information shared between the first and second detection areas.

In one embodiment, the systems and methods can operate through the use of mass identification techniques. In this embodiment, "mass" is identified and tracked. Unique identities can be assigned to masses by computer systems and tracked based on changes in radio signal characteristics. As a non-limiting example, if a mass is first detected near the center of the room and then a few feet away from the center of the room, the system did not detect any other mass entering the room, and the system did not 2 It can be assumed that the detected mass is the same mass as the first detected mass, but has been relocated to a new location. Based on the difference in signal properties caused by interference of masses in the network, the system can deduce, for example, that other masses exhibiting similar movements will have a similar effect on the signal properties. In this way, the system can "learn" how to identify and track masses.

Each human body mass in the system causes different interference characteristics when placed at any location for most indoor locations, but the overall human body set likely to exist in a room is generally finite. In other words, most of the indoor space changes in minor and rarely, most of the time, people of the same basic set occupy most of the space. For example, the same set of people appear every day in public places such as work, schools, and even restaurants. Since most indoor spaces can only enter a limited number of access points, such as doorways, the system can detect people entering and exiting the entry point and determine specific interference patterns due to the presence of a specific human body when entering or exiting the space. Based on the signal characteristics (interference) and how these characteristics change compared to other human bodies in space, it is possible to determine where and how each human body moves through the room.

In particular, it is contemplated that the system may automate various aspects of setup, particularly with respect to grouping nodes into detection regions and building nominal functional levels based on the machine learning methods described in this application. The system for determining the nearest node and estimating the detection area through inference does not require a user to set up. Based on the best estimates, the user can simply place nodes throughout the building, and the nodes are automatically grouped into detection areas using unsupervised machine learning to ultimately let the building system learn how to detect occupants. . Occupancy may be related to the actions taken by the occupant, and an automated system may be developed that can reduce or eliminate the need for human input for normal system operation.

In one embodiment, the method is a method for detecting the presence of a human body, the method comprising: providing a first transceiver disposed at a first location in a detection area; Providing a second transceiver disposed at a second location in the detection area; A computer server communicatively coupled to the first transceiver; The first transceiver receives a first set of radio signals from the second transceiver; The computer server comprising: receiving a first set of signal data from the first transceiver, wherein the first set of signal data includes data regarding characteristics of the first set of wireless signals; ; Estimating that the first set of signal data indicates the presence of a human body in the detection area; For wireless communication from the second transceiver to the first transceiver based at least in part on the characteristics of the first set of wireless signals in the first set of signal data when the human body is estimated to be present in the detection area Generating a detection signal profile; Receiving, by the first transceiver, a second set of radio signals from the second transceiver; The computer server includes: receiving a second set of signal data from the first transceiver that includes data regarding the characteristics of the second set of wireless signals; Estimating that the second set of signal data indicates that there is no human body in the detection area; Wireless communication from the second transceiver to the first transceiver based at least in part on the characteristics of the second set of wireless signals in the second set of signal data when any member of the human body is inferred within the detection area Generating a baseline signal profile for; The first transceiver receiving a third set of radio signals from the second transceiver; The computer server receives a third set of signal data from the first transceiver, and the third set of signal data includes data regarding characteristics of the third set of wireless signals; Determining whether the third set of signal data indicates the presence of a human body or the absence of any human body in the detection area, wherein the detection signal profile and the baseline signal profile and the third set of signal data And determining, based at least in part on the comparison.

In a further embodiment, the method comprises inferring that the first set of signal data indicates the presence of a human body in the detection area, where the inference is received by the first transceiver from other transceivers in the detection area. Based at least in part on a further set of signal data for the generated signals; And inferring that the second set of signal data indicates the absence of any human body in the detection area, the inference adding to signals received by the first transceiver from other transceivers in the detection area. Based at least in part on the set of signal data.

In a further embodiment, the method, the computer server stores a plurality of history data records indicating whether the human body has been determined to be present in the detection area for a period of time, each of the history data recording is present in the detection area And an indication of the number of the human body determined to do so and the date and time each of the number of human bodies determined to exist in the detection area; And the computer server making at least some of the plurality of historical data records available to one or more external computer systems through an interface.

In a further embodiment, the method comprises the computer server being operatively coupled to a second system; Only after the computer server determines that the human body is present in the detection area, the computer server includes operating the second system.

In a further embodiment, the method includes causing the first transceiver and the second system to be configured to communicate using the same communication protocol.

In a further embodiment, the method comprises the second system comprising an electrical system; Lighting system; Heating, exhaust and cooling (HVAC) systems; Security system; And to be selected from the group consisting of industrial automation systems and electrical systems.

In a further embodiment, the method includes the wireless communication using a protocol selected from the group consisting of Bluetooth, Bluetooth low energy, ANT, ANT +, WiFi, ZigBee, Thread and Z-Eve.

In a further embodiment, the method includes the wireless communication from the second transceiver to the first transceiver having a carrier frequency in a range covering 850 MHz to 17.5 GHz.

In a further embodiment, the method comprises allowing the computer server to determine whether the third set of signal data indicates the presence of a human body, including a confidence metric.

In a further embodiment, the method includes the first transceiver and the second transceiver configured to automatically calculate a relative position within the detection area.

In another embodiment, the method includes a method for estimating the number of human bodies present in the area, the method comprising: providing a first transceiver disposed at a first location in the detection area; Providing a second transceiver disposed at a second location in the detection area; A computer server communicatively coupled to the first transceiver; The first transceiver includes receiving a first set of radio signals from the second transceiver when the human body is not within the detection area; The computer server receives a first set of signal data from the first transceiver, and the first set of signal data includes data regarding characteristics of the first set of wireless signals; The computer server generates a baseline signal profile for wireless communication from the second transceiver to the first transceiver, and the baseline signal profile is the first set of signal data when a human body is not present in the detection area. In at least partially based on the characteristics of the first set of wireless signals; The first transceiver receives a second set of radio signals from the second transceiver when a first plurality of human bodies are present in the detection area, the first plurality of human bodies having a total mass; The computer server receives a second set of signal data from the first transceiver, and the second set of signal data includes data regarding characteristics of the second set of wireless signals; The computer server generates a second baseline signal profile for wireless communication from the second transceiver to the first transceiver, and the second baseline signal profile is when the first plurality of human bodies are present in the detection area. Based at least in part on characteristics of the second set of wireless signals in the second set of signal data; The first transceiver receives a third set of radio signals from the second transceiver when a second plurality of human bodies are present in the detection area; The computer server receives a third set of signal data from the first transceiver, and the third set of signal data includes data regarding characteristics of the third set of wireless signals; The computer server estimates the total mass of the second plurality of human bodies, and the estimation determines the characteristics of the third set of radio signals in the signal of the third radio set as the first baseline signal profile and the second Based at least in part on comparing with the baseline signal profile; The computer server estimates the total number of human bodies in the plurality of human bodies based at least in part on dividing the estimated total mass of the plurality of human bodies by the average mass per human body.

In a further embodiment, the method further includes: the computer server estimating the total mass of the second plurality of human bodies, the characteristics of the third set of wireless signals in the signal data of the third wireless set and the second set And based at least in part on a comparison of the characteristics of the second set of wireless signals in the wireless signal data.

In a further embodiment, the method includes: the first set of wireless signals, the second set of wireless signals, and the characteristics of the third set of wireless signals include wireless network signal protocol characteristics determined by the first transceiver. .

In a further embodiment, the method includes each of the wireless network signal protocol characteristics being selected from the group consisting of received signal strength, latency and bit error rate.

In a further embodiment, the method includes interpolating the total mass of the human body in the second plurality of human bodies that the computer server estimates.

In a further embodiment, the method, the interpolating step uses the mass 0 assumed for the baseline signal profile and the total mass of the first plurality of human bodies for the second baseline signal profile.

In a further embodiment, the method comprises the total mass being a discrete user-supplied quantity.

In a further embodiment, the method comprises the average mass per human body being a discrete user supply.

In a further embodiment, the method stores the computer server a plurality of history data records indicating whether the human body has been in the detection area for a period of time, each of the history data records being the number of human bodies detected in the detection area and Each of the number of the human body includes an indication of the date and time detected in the detection area; And the computer server making at least some of the plurality of historical data records available to one or more external computer systems through an interface.

In a further embodiment, the method is adjusted based on machine learning, wherein the computer server estimates the total mass of the second plurality of human bodies comprises: having a fiducial element in the detection area. Determining a first sample total mass of a plurality of human bodies, wherein the first sample mass is determined based on detection of the fiducial element; Determining a total mass of the second samples of the plurality of human bodies in the detection area, wherein the second sample mass is determined based at least in part on a comparison of the received second set of signal data and the baseline signal profile , Determining the total mass of the second sample; Comparing the first sample mass with the second sample mass; And adjusting an estimate of the total mass of the second plurality of human bodies based on the comparison.

In a further embodiment, the method is adjusted based on machine learning, wherein the computer server estimates the total mass of the second plurality of human bodies includes: Determining the total mass of the first sample; Determining the total mass of a second sample of a plurality of human bodies in the detection area, wherein the second sample mass is determined based at least in part on a comparison of the received second set of signal data and the baseline signal profile, Determining the total mass of the second sample; Comparing the first sample mass with the second sample mass; And adjusting the determination of the second sample mass based on the comparison.

In a further embodiment, the method may include determining a first sample position of the human body based on inference in the detection area, wherein the computer server is configured to detect the human body from the detected operation of the network element in the detection area. And inferring that it exists within the detection region of.

In a further embodiment, the method includes the network component being a component of an electrical system, lighting system, heating, ventilation and cooling (HVAC) system, security system or industrial automation system.

In a further embodiment, the method includes a reliability metric for estimating the total number of human bodies in the plurality of human bodies.

In a further embodiment, the method includes the first transceiver and the second transceiver configured to automatically calculate a relative position within the detection area.

In a further embodiment, the method includes the wireless communication using a protocol selected from the group consisting of Bluetooth, Bluetooth low energy, ANT, ANT +, WiFi, ZigBee, Thread and Z-Eve.

In a further embodiment, the method includes the wireless communication from the second transceiver to the first transceiver having a carrier frequency in a range covering 850 MHz to 17.5 GHz.

Although the present invention has been disclosed with descriptions of specific embodiments, including those currently deemed desirable, the detailed descriptions are for illustrative purposes and should not be understood as limiting the scope of the disclosure. As will be understood by those skilled in the art, embodiments other than those detailed in this application are included in the present invention. Modifications and modifications of the described embodiments can be made without departing from the spirit and scope of the invention.

Claims (75)

In the method of detecting the presence of the human body,
Providing a first transceiver disposed at a first location in the detection area;
Providing a second transceiver disposed at a second location in the detection area;
A computer server communicatively coupled to the first transceiver;
The first transceiver receives a first set of radio signals from the second transceiver when the human body is in a first position within the detection area;
The computer server receives a first set of signal data from the first transceiver, and the first set of signal data includes data regarding characteristics of the first set of wireless signals;
The computer server generates a baseline signal profile for wireless communication from the second transceiver to the first transceiver, and the baseline signal profile is the first when the human body is in the first position in the detection area. Based at least in part on characteristics of the first set of radio signals of the set of signal data;
The human body moves from the first position to the second position in the detection area;
The first transceiver receives a second set of radio signals from the second transceiver when the human body is in the second position;
The computer server receives a second set of signal data from the first transceiver, and the second set of signal data includes data regarding characteristics of the second set of wireless signals; And
The computer server determines whether the position of the human body has been changed in the detection area, and the determination compares characteristics of the second set of radio signals in the signal data of the second radio set to the baseline signal profile. A method comprising at least partially based. According to claim 1,
And the characteristics of the first set of radio signals include radio network signal protocol characteristics determined by the first transceiver. According to claim 2,
Each of the wireless network signal protocol characteristics is selected from the group consisting of received signal strength, latency, and bit error rate. According to claim 1,
The human body moves to a new position in the detection area;
The first transceiver receives a new set of radio signals from the second transceiver when the human body is in the detection area at the new location;
The computer server receives a new set of signal data from the first transceiver, and the new set of signal data includes data regarding characteristics of the new set of wireless signals;
The computer server replaces the baseline signal profile with a new baseline signal for wireless communication from the second transceiver to the first transceiver when the human body is in the new location, and the second baseline signal profile is And when the human body is present in the detection area at the new location, is generated based at least in part on the characteristics of the new set of radio signals of the new set of signal data. According to claim 4,
And before the computer server replaces the baseline signal profile with the new baseline signal, the computer server further comprises storing historical data records of the baseline profile. The method of claim 5,
The stored history data record indicates that the human body has been detected in the detection area, and includes a date and time when the human body has been detected in the detection area. According to claim 4,
The method of repeating the moving, receiving and replacing one or more times. According to claim 1,
The computer server determines whether the position of the human body has been changed in the detection area,
Determining a first sample position of a human body having a fiducial element in the detection area, wherein the first sample position is determined based on detection of the fiducial element, determining the first sample position ;
Determining a second sample position of the human body in the detection area, wherein the second sample position is determined based at least in part on a comparison of the received second set of signal data and the baseline signal profile; Determining a second sample location;
Comparing the first sample position and the second sample position; And
And adjusting the determination of the second sample location based on the comparison. According to claim 1,
The computer server determines whether the position of the human body has been changed in the detection area,
Determining a first sample position of the human body based on the inference of the detection area;
Determining a second sample position of the human body in the detection area, wherein the second sample position is determined based at least in part on a comparison of the received second set of signal data and the baseline signal profile; Determining a second sample location;
Comparing the first sample position and the second sample position; And
And adjusting the determination of the second sample location based on the comparison. The method of claim 9,
The step of determining the first sample position of the human body based on the inference in the detection area is the computer server that infers from the detected operation of the network element in the detection area that the human body is present in the detection area near the network element. How to include. The method of claim 10, wherein the network component is a component of an electrical system, lighting system, heating, ventilation and cooling (HVAC) system, security system or industrial automation system. According to claim 1,
The computer server stores a plurality of history data records indicating whether the human body has changed positions in the detection area over a period of time, and each of the history data records includes the number of human bodies detected in the detection area and the human body. Each of the numbers includes an indication of the date and time at which the position was determined to have changed in the detection area; And
And the computer server further comprising making at least some of the plurality of historical data records available to one or more external computer systems via an interface. According to claim 1,
The computer server is operatively coupled to a second system; And only after the computer server determines that a human body is present in the detection area, the computer server operates the second system. According to claim 3,
And the first transceiver and the second system are configured to communicate using the same communication protocol. The method of claim 13,
The second system includes an electrical system; Lighting system; Heating, exhaust and cooling (HVAC) systems; Security system; And selected from the group consisting of industrial automation systems. According to claim 1,
The wireless communication method using a protocol selected from the group consisting of Bluetooth, Bluetooth low energy, ANT, ANT +, WiFi, Zigbee, Thread and Z-wave. According to claim 1,
Wherein the wireless communication from the second transceiver to the first transceiver has a carrier frequency in a range covering 850 MHz to 17.5 GHz. According to claim 1,
And the computer server determining whether the position of the human body has been changed in the detection area comprises a confidence metric. According to claim 1,
And the first transceiver and the second transceiver are configured to automatically calculate a relative position within the detection area. According to claim 1,
And wherein the first transceiver and the second transceiver are configured to automatically define a detection area including the first transceiver and the second transceiver. In the method of detecting the presence of the human body,
Providing a first transceiver disposed at a first location in the detection area;
Providing a second transceiver disposed at a second location in the detection area;
A computer server communicatively coupled to the first transceiver;
The first transceiver receives a first set of radio signals from the second transceiver;
The computer server is:
Receiving a first set of signal data from the first transceiver, the first set of signal data comprising data relating to characteristics of the first set of wireless signals;
Estimating that the first set of signal data indicates the presence of a human body in the detection area;
For wireless communication from the second transceiver to the first transceiver based at least in part on the characteristics of the first set of wireless signals in the first set of signal data when the human body is estimated to be present in the detection area Generating a detection signal profile;
The first transceiver receives a second set of radio signals from the second transceiver;
The computer server is:
Receiving a second set of signal data from the first transceiver, the second set of signal data comprising data relating to characteristics of the second set of wireless signals;
Estimating that the second set of signal data indicates that there is no human body in the detection area;
Wireless communication from the second transceiver to the first transceiver based at least in part on the characteristics of the second set of wireless signals in the second set of signal data when any member of the human body is inferred within the detection area Generating a baseline signal profile for;
The first transceiver receives a third set of radio signals from the second transceiver;
The computer server receives a third set of signal data from the first transceiver, and the third set of signal data includes data regarding characteristics of the third set of wireless signals;
Determining whether the third set of signal data indicates the presence of a human body or the absence of any human body in the detection area, wherein the detection signal profile and the baseline signal profile and the third set of signal data And determining, based at least in part on the comparison. The method of claim 21,
In the step of inferring that the first set of signal data indicates the presence of a human body in the detection area, the inference is a further set of signals for signals received by the first transceiver from other transceivers in the detection area. Based at least in part on the data; And
In the step of inferring that the second set of signal data represents the absence of any human body in the detection area, the inference is an additional set of signals received by the first transceiver from other transceivers in the detection area. Based at least in part on the signal data of the method. The method of claim 21,
The computer server stores a plurality of history data records indicating whether the human body has been determined to be present in the detection area for a period of time, and each history data record is the number of the human body determined to exist in the detection area and the human body Each of the numbers includes an indication of the date and time determined to be present in the detection area; And
And the computer server further comprising making at least some of the plurality of historical data records available to one or more external computer systems via an interface. The method of claim 21,
The computer server is operatively coupled to a second system; And
And only after the computer server determines that a human body is present in the detection area, the computer server further comprises operating the second system. The method of claim 24,
And the first transceiver and the second system are configured to communicate using the same communication protocol. The method of claim 24,
The second system includes an electrical system; Lighting system; Heating, exhaust and cooling (HVAC) systems; Security system; Method selected from the group consisting of industrial automation systems and electrical systems. The method of claim 21,
The wireless communication uses a protocol selected from the group consisting of Bluetooth, Bluetooth low energy, ANT, ANT +, WiFi, ZigBee, Thread and Z-Wave. The method of claim 21,
Wherein the wireless communication from the second transceiver to the first transceiver has a carrier frequency in a range covering 850 MHz to 17.5 GHz. The method of claim 21,
And the computer server determining whether the third set of signal data indicates the presence of a human body comprises a confidence metric. The method of claim 21,
And the first transceiver and the second transceiver are configured to automatically calculate a relative position within the detection area. In the method for estimating the number of human body present in the region,
Providing a first transceiver disposed at a first location in the detection area;
Providing a second transceiver disposed at a second location in the detection area;
A computer server communicatively coupled to the first transceiver;
The first transceiver receives a first set of radio signals from the second transceiver when the human body is not within the detection area;
The computer server receives a first set of signal data from the first transceiver, and the first set of signal data includes data regarding characteristics of the first set of wireless signals;
The computer server generates a first baseline signal profile for wireless communication from the second transceiver to the first transceiver, and the first baseline signal profile is the first when the human body does not exist in the detection area. Based at least in part on characteristics of the first set of wireless signals in the set of signal data;
The first transceiver receives a second set of radio signals from the second transceiver when a first plurality of human bodies are present in the detection area, the first plurality of human bodies having a total mass;
The computer server receives a second set of signal data from the first transceiver, and the second set of signal data includes data regarding characteristics of the second set of wireless signals;
The computer server generates a second baseline signal profile for wireless communication from the second transceiver to the first transceiver, and the second baseline signal profile is when the first plurality of human bodies are present in the detection area. Based at least in part on characteristics of the second set of wireless signals in the second set of signal data;
The first transceiver receives a third set of radio signals from the second transceiver when a second plurality of human bodies are present in the detection area;
The computer server receives a third set of signal data from the first transceiver, and the third set of signal data includes data regarding characteristics of the third set of wireless signals;
The computer server estimates the total mass of the second plurality of human bodies, and the estimation determines the characteristics of the third set of radio signals in the signal of the third radio set as the first baseline signal profile and the second Based at least in part on comparing with the baseline signal profile;
And wherein the computer server estimates the total number of human bodies in the plurality of human bodies based at least in part on dividing the estimated total mass of the plurality of human bodies by an average mass per human body. The method of claim 31,
The computer server estimating the total mass of the second plurality of human body includes the characteristics of the third set of wireless signals in the third wireless set of signal data and the second set of the second set of wireless signal data. Method, based at least in part on a comparison of the characteristics of a radio signal. The method of claim 31,
And the characteristics of the first set of wireless signals, the second set of wireless signals, and the third set of wireless signals include wireless network signal protocol characteristics determined by the first transceiver. The method of claim 31,
Each of the wireless network signal protocol characteristics is selected from the group consisting of received signal strength, latency and bit error rate. The method of claim 31,
The computer server estimates include interpolating the total mass of the human body in the second plurality of human bodies. The method of claim 31,
Wherein the interpolation uses the mass 0 assumed for the baseline signal profile and the total mass of the first plurality of human bodies for the second baseline signal profile. The method of claim 31,
Wherein the total mass is a user-supplied quantity. 32. The method of claim 31, wherein the average mass per human body is a discrete user supply. The method of claim 31,
The computer server stores a plurality of history data records indicating whether or not the human body has been in the detection area for a period of time, and each of the history data records is detected by the number of human bodies and the number of human bodies detected in the detection area, respectively. Includes an indication of the date and time detected in the area; And
And the computer server further comprising making at least some of the plurality of historical data records available to one or more external computer systems via an interface. The method of claim 31,
The computer server estimates the total mass of the second plurality of human bodies,
Determining a first sample total mass of a plurality of human bodies having a fiducial element in the detection area, wherein the first sample mass is determined based on detection of the fiducial element, the first sample total mass Determining;
Determining a total mass of the second samples of the plurality of human bodies in the detection area, wherein the second sample mass is determined based at least in part on a comparison of the received second set of signal data and the baseline signal profile , Determining the total mass of the second sample;
Comparing the first sample mass with the second sample mass; And
And adjusting the estimate of the total mass of the second plurality of human bodies based on the comparison based on machine learning. The method of claim 31,
The computer server estimates the total mass of the second plurality of human bodies,
Determining a first sample total mass of a plurality of human bodies based on inference in the detection area;
Determining the total mass of a second sample of a plurality of human bodies in the detection area, wherein the second sample mass is determined based at least in part on a comparison of the received second set of signal data and the baseline signal profile, Determining the total mass of the second sample;
Comparing the first sample mass with the second sample mass; And
And adjusting the determination of the second sample mass based on the comparison. The method of claim 21,
The step of determining the first sample position of the human body based on the inference in the detection area is the computer server that infers from the detected operation of the network element in the detection area that the human body is present in the detection area near the network element. How to include. The method of claim 22,
The network component is a component of an electrical system, lighting system, heating, ventilation and cooling (HVAC) system, security system or industrial automation system. The method of claim 31,
The method for estimating the total number of human body in the plurality of human body includes a reliability metric. The method of claim 31,
And the first transceiver and the second transceiver are configured to automatically calculate a relative position within the detection area. The method of claim 31,
The wireless communication uses a protocol selected from the group consisting of Bluetooth, Bluetooth low energy, ANT, ANT +, WiFi, ZigBee, Thread and Z-Wave. The method of claim 31,
Wherein the wireless communication from the second transceiver to the first transceiver has a carrier frequency in a range covering 850 MHz to 17.5 GHz. In the method for detecting the presence of an object that affects the RF signal,
Providing a first transceiver disposed at a first location in the detection area;
Providing a second transceiver disposed at a second location in the detection area; And
A computer server communicatively coupled to the first transceiver;
The first transceiver receives a first set of radio signals from the second transceiver when an object affecting the RF signal is present in the detection area at a first location;
The computer server receives a first set of signal data from the first transceiver, and the first set of signal data includes data regarding characteristics of the first set of wireless signals;
The computer server generates a baseline signal profile for wireless communication from the second transceiver to the first transceiver, and the baseline signal profile is the first location where the object affecting the RF signal is located in the detection area. Based at least in part on the characteristics of the first set of radio signals of the first set of signal data when present in;
The object moves from the first position to the second position in the detection area;
The first transceiver receives a second set of radio signals from the second transceiver when the object affecting the RF signal is present in the second location;
The computer server receives a second set of signal data from the first transceiver, and the second set of signal data includes data regarding characteristics of the second set of wireless signals; And,
The computer server determines whether the position of the object affecting the RF signal in the detection area has changed, and the determination is made of the characteristics and the base of the second set of radio signals in the signal data of the second radio set. And based at least in part on a comparison of the line signal profile. The method of claim 48,
And the characteristics of the first set of radio signals include radio network signal protocol characteristics determined by the first transceiver. The method of claim 49,
Each of the wireless network signal protocol characteristics is selected from the group consisting of received signal strength, latency and bit error rate. The method of claim 48,
The method of affecting an RF signal, the method comprising a weapon. The method of claim 48,
The object affecting the RF signal moves to a new location in the detection area;
The first transceiver receives a new set of radio signals from the second transceiver when the object affecting the RF signal is present in the detection area at the new location;
The computer server receives a new set of signal data from the first transceiver, and the new set of signal data includes data regarding characteristics of the new set of wireless signals;
The computer server replaces the baseline signal profile with a new baseline signal for wireless communication from the second transceiver to the first transceiver when the object affecting the RF signal is in the new location, and the 2 the baseline signal profile further comprises being generated based at least in part on the characteristics of the new set of radio signals of the new set of signal data when the object affecting the RF signal is present in the detection area at the new location How to. The method of claim 52,
And before the computer server replaces the baseline signal profile with the new baseline signal, the computer server further comprises storing historical data records of the baseline profile. The method of claim 53,
The stored history data record indicates that the object affecting the RF signal has been detected in the detection area, and includes the date and time the object was detected in the detection area. The method of claim 52,
The method of repeating that the object affecting the RF signal moves, the server receives, and the server is replaced at least twice. The method of claim 48,
The computer server determines whether the position of the object affecting the RF signal in the detection area has been changed,
Determining a first sample position of an object affecting an RF signal having a fiducial element in the detection area, wherein the first sample position is determined based on detection of the fiducial element Determining a sample location;
Determining a second sample position of the object influencing the RF signal in the detection area, the second sample position being the baseline not using the received second set of signal data and the fiducial element profile Determining the second sample location, which is determined based at least in part on a comparison of a signal profile;
Comparing the first sample position and the second sample position; And
Adjusting the determining step of the second sample position to improve the position calculation capability of the system, wherein the adjusting is based on a comparison between the first and second decisions, wherein the adjusting comprises: Adjusted based on how. The method of claim 48,
And the existing historical record data of the first signal data and the second signal data are retrieved from a server and adjusted to allow use of the historical record in different operating environments. The method of claim 48,
The method of influencing the RF signal, wherein the object comprises a metal. The method of claim 48,
Wherein the object causes a measurable change in signal metric due to one or more phenomena from the list of reflections, refractions, diffractions, scattering and absorption. The method of claim 48,
The method affecting RF is a vehicle. A method for correlating detection of human activity in space with desirable system behavior, comprising:
A training step in which the system is provided with a complete set of training data of human activity and system input;
The human body activity includes one or more of a change in the position of one or more human body, the presence of one or more human body, a count of the human body in the physical space, and / or a position of the human body in the physical space;
As a model building phase,
The system calculates an optimal relationship between the system input and the human activity; And
The system predicting an action to be taken based on the optimal relationship, the model building step; And
And an action step wherein the system performs a predicted action. The method of claim 61,
The training step and the model building step are repeated before the action step. The method of claim 61,
The system input includes at least one of time, calendar date, day of the week, current weather or predicted weather. The method of claim 63,
Wherein the system input comprises a comparison of a new system input with an old system input. The method of claim 61,
Wherein the system input includes output from a sensor. The method of claim 65,
The sensor is selected from the group consisting of a light sensor, a temperature sensor, a carbon dioxide sensor or a location beacon. In the method of estimating the number of human body in space,
Providing a plurality of presence sensing networks in the space, each of the presence sensing networks can provide at least one data element for a detection area in the space specific to the network, each of the data elements being: the specific Selected from the group consisting of detecting the presence of at least one person in a detection area, determining the number of persons in the detection area, and calculating a metric correlated with the average number of persons in the specific detection area within a specific time period, Providing the sensing network; And
And aggregating all of the data elements from all of the presence sensing networks to provide an overall estimate of the human body in the space. The method of claim 67,
The result of the aggregation is time stamped and recorded periodically. The method of claim 67,
The aggregate result is provided to the requesting party. The method of claim 69,
Wherein the requesting party comprises a first responder to an emergency in the space. The method of claim 69,
The data element is presented as one or more of a map, a real audible or visual signal, an overlay on a display, or orientation information in a virtual reality display. The method of claim 69,
The result is periodically updated with new information from the plurality of presence sensing networks. The method of claim 72,
And the result indicates when one of the presence sensing networks is no longer transmitting a data element. The method of claim 69,
The requesting party comprises a robot. The method of claim 69,
The result also includes information provided from other sensors in the space and information from sensors outside the space.

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