WO2025049109A2 - Communication de données par l'intermédiaire de nœuds de réseau mobile dans des environnements de surveillance compartimentés - Google Patents

Communication de données par l'intermédiaire de nœuds de réseau mobile dans des environnements de surveillance compartimentés Download PDF

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Publication number
WO2025049109A2
WO2025049109A2 PCT/US2024/042294 US2024042294W WO2025049109A2 WO 2025049109 A2 WO2025049109 A2 WO 2025049109A2 US 2024042294 W US2024042294 W US 2024042294W WO 2025049109 A2 WO2025049109 A2 WO 2025049109A2
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Prior art keywords
network
compartmentalized
network node
network nodes
data
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WO2025049109A3 (fr
Inventor
Landy Toth
Kevin D'AQUILLA
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Lifelens Technologies Inc
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Lifelens Technologies Inc
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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/021—Traffic management, e.g. flow control or congestion control in wireless networks with changing topologies, e.g. ad-hoc networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/0215—Traffic management, e.g. flow control or congestion control based on user or device properties, e.g. MTC-capable devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/18—Communication route or path selection, e.g. power-based or shortest path routing based on predicted events
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location

Definitions

  • the present disclosure relates to the field of physiologic monitoring and, more particularly, to devices and systems for physiologic monitoring.
  • Illustrative embodiments provide techniques for communication of data via mobile network nodes in compartmentalized monitoring environments.
  • a system comprises a compartmentalized monitoring environment comprising two or more compartments, at least one inter-compartment connection interconnecting a first one of the two or more compartments and a second one of the two or more compartments, and a plurality of network nodes.
  • the plurality of network nodes are part of a mesh network for the compartmentalized monitoring environment.
  • the plurality of network nodes are configured for communicating data between the two or more compartments of the compartmentalized monitoring environment.
  • Each network node in the plurality of network nodes may be associated with at least one of a set of two or more network node roles for at least a portion of a designated period of time. At least a given one of the plurality of network nodes may be associated with a first one of the set of two or more network node roles for a first portion of the designated period of time and may be associated with a second one of the two or more network node roles for a second portion of the designated period of time.
  • the set of two or more network node roles may comprise an intra-compartment mobile network node role associated with ones of the plurality of network nodes having mobility limited to a single one of the two or more compartments of the compartmentalized monitoring environment for at least a portion of the designated period of time, and an intercompartment mobile network node role associated with ones of the plurality of network nodes having mobility across at least two of the two or more compartments of the compartmentalized monitoring environment for at least a portion of the designated period of time.
  • the set of two or more network node roles may further comprise a stationary network node role associated with ones of the plurality of network nodes in a fixed position within one of the two or more compartments of the compartmentalized monitoring environment for at least a portion of the designated period of time.
  • a given one of the plurality of network nodes associated with the stationary network node role may provide an anchor point for establishing location and movement patterns of one or more other ones of the plurality of network nodes in the compartmentalized monitoring environment.
  • a given one of the plurality of network nodes associated with the stationary network node role may also or alternatively be configured to charge at least one other one of the plurality of network nodes.
  • the set of two or more network node roles may further comprise a compartmentalized network sink node role associated with ones of the plurality of network nodes capable of establishing a data link, distinct from the mesh network, with at least one external network outside the compartmentalized monitoring environment for at least a portion of the designated period of time.
  • At least a given one of the plurality of network nodes may be associated with the compartmentalized network sink node role and one of the intra-compartment mobile network node role and the inter-compartment mobile network node role for at least a portion of the designated period of time.
  • At least a given one of the plurality of network nodes may comprise a smart device associated with a given user, the smart device comprising one of a smartphone and tablet computing device, a wearable device associated with a given user, a gateway device associated with a given user, a standalone wireless access point, an edge computing device, a sensor device (e.g., an Internet of Things (loT) sensor device), a carrying case of a rapid deployment physiologic monitoring kit, etc.
  • a smart device associated with a given user comprising one of a smartphone and tablet computing device, a wearable device associated with a given user, a gateway device associated with a given user, a standalone wireless access point, an edge computing device, a sensor device (e.g., an Internet of Things (loT) sensor device), a carrying case of a rapid deployment physiologic monitoring kit, etc.
  • the smart device comprising one of a smartphone and tablet computing device, a wearable device associated with a given user, a gateway device associated with a given user,
  • the compartmentalized monitoring environment may comprise a facility, the two or more compartments may comprise at least one of rooms and floors of the facility, and the at least one inter-compartment connection may comprise at least one of a door, a hatch, a hallway, and a stairway in the facility.
  • the two or more compartments may comprise two or more geographic regions of a remote monitoring environment, at least one of the two or more geographic regions having no or limited connectivity to an external network outside the remote monitoring environment.
  • the mesh network may be formed on one or more physical layers, each of the one or more physical layers comprising a range of frequency bands upon which data can be transferred, and wherein the mesh network utilizes at least one mesh protocol based on at least one of a long range (LoRa) evolution schema, an ultrawideband (UWB) mesh protocol, a Bluetooth Low Energy (BLE) mesh protocol, a WiFi mesh protocol, a HaLow mesh protocol, and a private 5G mesh protocol.
  • LiRa long range
  • UWB ultrawideband
  • BLE Bluetooth Low Energy
  • At least a first one of the plurality of network nodes may be configured to obtain data from a second one of the plurality of network nodes which is destined for a third one of the plurality of network nodes, the first network node being configured to store the obtained data until the first network node hands off the obtained data to the third network node or a fourth network node that is predicted to be within range of the third network node before the first network node is predicted to be within range of the third network node.
  • the third network node may comprise a compartmentalized network sink node capable of establishing a data link, distinct from the mesh network, with at least one external network outside the compartmentalized monitoring environment.
  • the first network node may be configured to provide a transmission receipt to the second network node.
  • the third network node may be configured to perform analysis utilizing the obtained data from the second network node and additional obtained data from one or more additional ones of the plurality of network nodes.
  • the analysis may comprise a fusion of sensor data from a plurality of sensors associated with at least a subset of the plurality of network nodes including the second network node and the one or more additional ones of the plurality of network nodes.
  • Each of the plurality of network nodes in the compartmentalized monitoring environment may be assigned a mobility ranking score, the mobility ranking score for a given one of the plurality of network nodes being assigned based at least in part on how the given network node interacts with other ones of the plurality of network nodes in the compartmentalized monitoring environment.
  • the mobility ranking score for the given network node may be determined based at least in part on how many other ones of the plurality of network nodes come within range of the given network node over a designated period of time.
  • the mobility ranking score for the given network node may also or alternatively be determined based at least in part on a number of network hops between the given network node and a closest other one of the plurality of network nodes which comprises a compartmentalized network sink node capable of establishing a data link, distinct from the mesh network, with at least one external network outside the compartmentalized monitoring environment.
  • the data may be communicated between the two or more compartments of the compartmentalized monitoring environment between different ones of the plurality of network nodes selected based at least in part on their assigned mobility ranking scores.
  • the mobility ranking scores assigned to the plurality of network nodes may be dynamically updated over time.
  • an apparatus comprises at least one processing device comprising a processor coupled to a memory.
  • the at least one processing device implements a given network node of a plurality of network nodes in a compartmentalized monitoring environment comprising two or more compartments.
  • the at least one processing device is configured to join a mesh network for the compartmentalized monitoring environment, the mesh network comprising the plurality of network nodes, and to communicate data between the two or more compartments of the compartmentalized monitoring environment by at least one of obtaining at least a portion of the data from and providing at least a portion of the data to at least one other network node of the plurality of network nodes that is within range of the given network node in the mesh network.
  • the given network node may be associated with at least one of a set of two or more network node roles for at least a portion of a designated period of time.
  • the given network node may be associated with a first one of the set of two or more network node roles for a first portion of the designated period of time and is associated with a second one of the two or more network node roles for a second portion of the designated period of time.
  • the given network node may be configured to obtain data from a first other one of the plurality of network nodes which is destined for a second other one of the plurality of network nodes, the given network node being configured to store the obtained data until the given network node hands off the obtained data to the second other network node or a third other network node that is predicted to be within range of the second other network node before the given network node is predicted to be within range of the second other network node.
  • the second other network node may comprise a compartmentalized network sink node capable of establishing a data link, distinct from the mesh network, with at least one external network outside the compartmentalized monitoring environment.
  • the at least one processing device may be further configured to determine mobility ranking scores assigned to at least a subset of the plurality of network nodes in the compartmentalized monitoring environment, the mobility ranking score for at least one of the plurality of network nodes being assigned based at least in part on how said at least one of the plurality of network nodes interacts with other ones of the plurality of network nodes in the compartmentalized monitoring environment.
  • the mobility ranking score for said at least one of the plurality of network nodes may be determined based at least in part on how many other ones of the plurality of network nodes come within range of said at least one of the plurality of network nodes over a designated period of time.
  • the mobility ranking score for said at least one of the plurality of network nodes may also or alternatively be determined based at least in part on a number of network hops between said at least one of the plurality of network nodes and a closest other one of the plurality of network nodes which comprises a compartmentalized network sink node capable of establishing a data link, distinct from the mesh network, with at least one external network outside the compartmentalized monitoring environment.
  • the data may be communicated between the two or more compartments of the compartmentalized monitoring environment using ones of the plurality of network nodes selected based at least in part on the mobility ranking scores assigned to the subset of the plurality of network nodes.
  • a method performed by a given network node of a plurality of network nodes in a compartmentalized monitoring environment comprising two or more compartments comprises joining a mesh network for the compartmentalized monitoring environment, the mesh network comprising the plurality of network nodes, and communicating data between the two or more compartments of the compartmentalized monitoring environment by at least one of obtaining at least a portion of the data from and providing at least a portion of the data to at least one other network node of the plurality of network nodes that is within range of the given network node in the mesh network.
  • the given network node comprises at least one processing device comprising a processor coupled to a memory.
  • the given network node may be associated with at least one of a set of two or more network node roles for at least a portion of a designated period of time, the set of two or more network node roles comprising: an intra-compartment mobile network node role associated with ones of the plurality of network nodes having mobility limited to a single one of the two or more compartments of the compartmentalized monitoring environment for at least a portion of the designated period of time; an inter-compartment mobile network node role associated with ones of the plurality of network nodes having mobility across at least two of the two or more compartments of the compartmentalized monitoring environment for at least a portion of the designated period of time; a stationary network node role associated with ones of the plurality of network nodes in a fixed position within one of the two or more compartments of the compartmentalized monitoring environment for at least a portion of the designated period of time; and a compartmentalized network sink node role associated with ones of the plurality of network nodes capable of establishing a data link, distinct from the mesh network
  • the mesh network may be formed on one or more physical layers, each of the one or more physical layers comprising a range of frequency bands upon which data can be transferred, and wherein the mesh network utilizes at least one mesh protocol based on at least one of a long range (LoRa) evolution schema, an ultrawideband (UWB) mesh protocol, a Bluetooth Low Energy (BLE) mesh protocol, a WiFi mesh protocol, a HaLow mesh protocol, and a private 5G mesh protocol.
  • LiRa long range
  • UWB ultrawideband
  • BLE Bluetooth Low Energy
  • the mobility ranking score for said at least one of the plurality of network nodes may be determined based at least in part on how many other ones of the plurality of network nodes come within range of said at least one of the plurality of network nodes over a designated period of time.
  • the mobility ranking score for said at least one of the plurality of network nodes may also or alternatively be determined based at least in part on a number of network hops between said at least one of the plurality of network nodes and a closest other one of the plurality of network nodes which comprises a compartmentalized network sink node capable of establishing a data link, distinct from the mesh network, with at least one external network outside the compartmentalized monitoring environment.
  • the given network node may be associated with at least one of a set of two or more network node roles for at least a portion of a designated period of time, the set of two or more network node roles comprising: an intra-compartment mobile network node role associated with ones of the plurality of network nodes having mobility limited to a single one of the two or more compartments of the compartmentalized monitoring environment for at least a portion of the designated period of time; an inter-compartment mobile network node role associated with ones of the plurality of network nodes having mobility across at least two of the two or more compartments of the compartmentalized monitoring environment for at least a portion of the designated period of time; a stationary network node role associated with ones of the plurality of network nodes in a fixed position within one of the two or more compartments of the compartmentalized monitoring environment for at least a portion of the designated period of time; and a compartmentalized network sink node role associated with ones of the plurality of network nodes capable of establishing a data link, distinct from the mesh network
  • the given network node may be configured to obtain data from a first other one of the plurality of network nodes which is destined for a second other one of the plurality of network nodes, the given network node being configured to store the obtained data until the given network node hands off the obtained data to the second other network node or a third other network node that is predicted to be within range of the second other network node before the given network node is predicted to be within range of the second other network node.
  • the mobility ranking score for said at least one of the plurality of network nodes may be determined based at least in part on how many other ones of the plurality of network nodes come within range of said at least one of the plurality of network nodes over a designated period of time.
  • the mobility ranking score for said at least one of the plurality of network nodes may be determined based at least in part on a number of network hops between said at least one of the plurality of network nodes and a closest other one of the plurality of network nodes which comprises a compartmentalized network sink node capable of establishing a data link, distinct from the mesh network, with at least one external network outside the compartmentalized monitoring environment.
  • illustrative embodiments enable communication of data in compartmentalized monitoring environments via mobile network nodes which travel between compartments of the compartmentalized monitoring environments.
  • FIG. 1 illustrates aspects of a modular physiological monitoring system, according to an embodiment of the invention.
  • FIGS. 2A-2D illustrate a modular physiological monitoring system, according to an embodiment of the invention.
  • FIGS. 3A-3F illustrate a wearable sensor system configured for physiologic monitoring of subjects in a compartmentalized monitoring environment, according to an embodiment of the invention.
  • FIG. 5 illustrates a process flow for communication of data in a compartmentalized monitoring environment via one or more mobile network nodes which travel between compartments of the compartmentalized monitoring environment, according to an embodiment of the invention.
  • One illustrative, non-limiting objective of this disclosure is to provide systems, devices, methods, and kits for physiologic monitoring of a subject.
  • Another illustrative, nonlimiting objective of this disclosure is to provide systems, devices, and methods for managing networks, including body area networks including different types of devices configured for physiologic monitoring of a subject, including monitoring of contextual and environmental information regarding an environment that the subject is in.
  • Another illustrative, non-limiting objective is to provide a flexible architecture enabling sharing of contextual and environmental information about different types of devices that are part of a body area network associated with a subject.
  • a modular physiological monitoring system in accordance with the present disclosure is configured to monitor one or more physiological and/or physical signals, also referred to herein as physiological parameters, of a subject (e.g., a human subject, a patient, a soldier, an athlete, a trainer, an animal such as equine, canine, porcine, bovine, etc.).
  • the modular physiological monitoring system may include one or more patches, each patch adapted for attachment to the body of the subject (e.g., attachable to the skin thereof, reversibly attachable, adhesively attachable, with a disposable interface and a reusable module, etc.).
  • the physiological monitoring system may also include one or more modules (also referred to as hubs in some illustrative embodiments), configured and dimensioned to mate with corresponding ones of the one or more patches, and to interface with the subject therethrough.
  • One or more of the modules may be configured to convey and/or store one or more physiological and/or physical signals, signals derived therefrom, and/or metrics derived therefrom obtained via the interface with the subject.
  • Each module may include a power source (e.g., a battery, a rechargeable battery, an energy harvesting transducer, microcircuit, an energy reservoir, a thermal gradient harvesting transducer, a kinetic energy harvesting transducer, a radio frequency energy harvesting transducer, a fuel cell, a biofuel cell, etc.), signal conditioning circuitry, communication circuitry, one or more sensors, or the like, configured to generate one or more signals (e.g., physiological and/or physical signals), stimulus, etc.
  • a power source e.g., a battery, a rechargeable battery, an energy harvesting transducer, microcircuit, an energy reservoir, a thermal gradient harvesting transducer, a kinetic energy harvesting transducer, a radio frequency energy harvesting transducer, a fuel cell, a biofuel cell, etc.
  • signal conditioning circuitry e.g., a signal conditioning circuitry, communication circuitry, one or more sensors, or the like, configured to generate one or more signals (e.g
  • One or more of the patches may include one or more interconnects, configured and dimensioned so as to couple with one or more of the modules, said modules including a complementary interconnect configured and dimensioned to couple with the corresponding patch.
  • the patch may include a bioadhesive interface for attachment to the subject, the module retainable against the subject via interconnection with the patch.
  • the patch may be configured so as to be single use (e.g., disposable).
  • the patch may include a thin, breathable, stretchable laminate.
  • the laminate may include a substrate, a bioadhesive, one or more sensing or stimulating elements in accordance with the present disclosure, and one or more interconnects for coupling one or more of the sensing elements with a corresponding module.
  • the patch may be sufficiently thin and frail, such that it may not substantially retain a predetermined shape while free standing. Such a definition is described in further detail below.
  • the patch may be provided with a temporary stiffening film to retain the shape thereof prior to placement of the patch onto the body of a subject. Once adhered to the subject, the temporary stiffening film may be removed from the patch. While the patch is adhered to the subject, the shape and functionality of the patch may be substantially retained.
  • the now freestanding patch is sufficiently frail such that the patch can no longer substantially retain the predetermined shape (e.g., sufficiently frail such that the patch will not survive in a free standing state).
  • stretch applied to the patch while removing the patch from the subject may result in snap back once the patch is in a freestanding state that renders such a patch to crumple into a ball and no longer function.
  • Removal of the patch from the skin of the subject may result in a permanent loss in shape of the patch without tearing of the patch.
  • the interconnect may be sufficiently frail such that removal of the patch from the skin of the subject may result in a permanent loss of shape of the interconnect.
  • the patch may include a film (e.g., a substrate), with sufficiently high tear strength, such that, as the patch is peeled from the skin of a subject, the patch does not tear.
  • the ratio between the tear strength of the patch and the peel adhesion strength of the patch to skin e.g., tear strength: peel adhesion strength
  • tear strength: peel adhesion strength is greater than 8: 1, greater than 4: 1, greater than 2: 1, or the like.
  • the patch may include a bioadhesive with peel tack to mammalian skin of greater than 0.02 Newtons per millimeter (N/mm), greater than O.lN/mm, greater than 0.25N/mm, greater than 0.50N/mm, greater than 0.75N/mm, greater than 2N/mm, or the like.
  • peel tack may be approximately determined using an American Society for Testing and Materials (ASTM) standard test, ASTM D3330: Standard test method for peel adhesion of pressure-sensitive tape.
  • the patch may exhibit a tear strength of greater than 0.5N/mm, greater than IN/mm, greater than 2N/mm, greater than 8N/mm, or the like.
  • tear strength may be approximately determined using an ASTM standard test, ASTM D624: Standard test method for tear strength of conventional vulcanized rubber and thermoplastic elastomers.
  • a patch in accordance with the present disclosure may have a ratio between the tear strength of the patch and the peel tack of the adhesive to mammalian skin is greater than 8: 1, greater than 4: 1, greater than 2: 1, or the like.
  • the patch may be provided with a characteristic thickness of less than 50 micrometer (pm), less than 25pm, less than 12pm, less than 8pm, less than 4pm, or the like. Yet, in aspects, a balance between the thickness, stiffness, and tear strength may be obtained so as to maintain sufficiently high comfort levels for a subject, minimizing skin stresses during use (e.g., minimizing skin stretch related discomfort and extraneous signals as the body moves locally around the patch during use), minimizing impact on skin health, minimizing risk of rucking during use, and minimizing risk of maceration to the skin of a subject, while limiting risk of tearing of the patch during removal from a subject, etc.
  • a balance between the thickness, stiffness, and tear strength may be obtained so as to maintain sufficiently high comfort levels for a subject, minimizing skin stresses during use (e.g., minimizing skin stretch related discomfort and extraneous signals as the body moves locally around the patch during use), minimizing impact on skin health, minimizing risk of rucking during use, and minimizing risk of macer
  • the properties of the patch may be further altered so as to balance the hydration levels of one or more hydrophilic or amphiphilic components of the patch while attached to a subject.
  • Such adjustment may be advantageous to prevent over hydration or drying of an ionically conducting component of the patch, to manage heat transfer coefficients within one or more elements of the patch, to manage salt absorption into a reservoir in accordance with the present disclosure, and/or migration during exercise, to prevent pooling of exudates, sweat, or the like into a fluid measuring sensor incorporated into the patch or associated module, etc.
  • the patch or a rate determining component thereof may be configured with a moisture vapor transmission rate of between 200 grams per meter squared per 24 hours (g/m 2 /24hrs) and 20,000g/m 2 /24hrs, between 500g/m 2 /24hrs and 12,000g/m 2 /24hrs, between 2,000g/m 2 /24hrs and 8,000g/m 2 /24hrs, or the like.
  • a moisture vapor transmission rate of between 200 grams per meter squared per 24 hours (g/m 2 /24hrs) and 20,000g/m 2 /24hrs, between 500g/m 2 /24hrs and 12,000g/m 2 /24hrs, between 2,000g/m 2 /24hrs and 8,000g/m 2 /24hrs, or the like.
  • one or more patches and/or modules may be configured for electrically conducting interconnection, inductively coupled interconnection, capacitively coupled interconnection, with each other.
  • each patch and module interconnect may include complementary electrically conducting connectors, configured and dimensioned so as to mate together upon attachment.
  • the patch and module may include complementary coils or electrodes configured and dimensioned so as to mate together upon attachment.
  • Each patch or patch-module set may be configured as a sensing device to monitor one or more local physiological and/or physical parameters of the attached subject (e.g., local to the site of attachment, etc.), local environment, combinations thereof, or the like, and to relay such information in the form of signals to a host device (e.g., via a wireless connection, via a body area network connection, or the like), one or more patches or modules on the subject, or the like.
  • a host device e.g., via a wireless connection, via a body area network connection, or the like
  • Each patch and/or patch-module set may also or alternatively be configured as a stimulating device to apply a stimulus to the subject in response to signaling from the host device and/or other source, the signaling being based on analysis of the physiological and/or physical parameters of the subject measured by the sensing device(s).
  • the patch or patch-module sets are examples of what are more generally referred to herein as “primary” sensing devices, which are advantageously designed as on-body sensing devices with a small form factor as part of the modular monitoring system. While such primary sensing devices may be used to obtain some desired information (e.g., local physiological and/or physical parameters of the attached subject, local environment, combinations thereof, etc.), in some cases it is beneficial to obtain contextual information from other types of sensors which are difficult to integrate into such primary sensing devices designed as on-body sensing devices with small form factors. Such other types of sensors may be integrated into “secondary” or accessory sensing devices that do not have the limitations of the “primary” sensing devices.
  • the primary sensing devices may be designed as on-body sensing devices with a small form factor for comfortable long-term wear by the subject
  • the secondary or accessory sensing devices may have larger form factors to accommodate different types of sensors than the primary sensing devices.
  • the secondary or accessory sensing devices may be incorporated into equipment or gear that is carried by a subject, into one or more wearable computing devices, a carrying case of a rapid care delivery deployment kit, stationary nodes or compartmentalized network sink nodes in compartmentalized monitoring environments, etc.
  • an accessory sensing device is directly attached to the body of the subject.
  • primary sensing devices can be attached or otherwise incorporated in equipment or gear carried by the subject, including but not limited to components of rapid care delivery deployment kits.
  • the on-body physiological monitoring or other primary sensing devices can benefit from additional contextual and environmental information about the conditions surrounding a subject under study, where the additional contextual and environmental information may be obtained from one or more accessory sensing devices.
  • the primary sensing devices may be used to acquire one or more physiological metrics of the subject such as heart rate, core temperature, respiratory cycle status, etc.
  • physiological metric data may be augmented by contextual or environmental data obtained using primary sensing devices and/or additional external sensing capabilities of accessory sensing devices, where the devices may target exposure of the subject to infectious agents, insolation, etc.
  • This contextualization capability may, under some circumstances, need to be flexible, requiring different sensing modalities at different times with different subjects under study.
  • sensors may not be easily integrated into a single primary (e.g., on-body) sensing device with a small form factor, and thus may need to be externalized into one or more accessory sensing devices that may be placed at different locations relative to the primary sensing devices on the same individual.
  • These various primary and accessory sensing devices may require a dedicated body area network to manage their functions, to enable efficient data sharing among them, and to facilitate contextual analysis of the different data obtained therefrom.
  • the host device may be configured to coordinate information exchange to/from each module and/or patch or other on-body primary sensing device as well as accessory sensing devices that are part of a body area network associated with a subject, and to generate one or more physiological signals, physical signals, environmental signals, kinetic signals, diagnostic signals, alerts, reports, recommendation signals, commands, combinations thereof, or the like for the subject, a user, a network, an electronic health record (EHR), a database (e.g., as part of a data management center, an electronic health record or EHR, a social network, an field operational network, etc.), a processor, combinations thereof, or the like.
  • EHR electronic health record
  • a database e.g., as part of a data management center, an electronic health record or EHR, a social network, an field operational network, etc.
  • the host device may include features for recharging and/or performing diagnostic tests on one or more of the modules.
  • a host device in accordance with the present disclosure may be integrated into a subject’s equipment or gear, housed in an accessory, within a purse, a backpack, a wallet, or may be included in a mobile computing device, a smartphone, a tablet computer, a pager, a laptop, a local router, a data recorder, a network hub, a server, a secondary mobile computing device, a repeater, a combination thereof, or the like.
  • the host device may also be part of a carrying case in a rapid care delivery deployment kit.
  • a system in accordance with the present disclosure may include a plurality of substantially similar modules (e.g., generally interchangeable modules, but with unique identifiers), for coupling with a plurality of patches, each patch, optionally different from the other patches in the system (e.g., potentially including alternative sensors, sensor types, sensor configurations, electrodes, electrode configurations, etc.).
  • Each patch may include an interconnect suitable for attachment to an associated module.
  • the module may validate the type and operation of the patch to which it has been mated.
  • the module may then initiate monitoring operations on the subject via the attached patch, communicate with one or more other patches and/or modules on the subject, a wireless gateway or other host device, etc.
  • the data collection from each module may be coordinated through one or more modules and/or with a host device in accordance with the present disclosure.
  • the modules may report a timestamp along with the data in order to synchronize data collection across multiple patch-module sets on the subject, between subjects, etc.
  • a hot swappable replacement e.g., replacement during a monitoring procedure
  • Such a configuration may be advantageous for performing redundant, continuous monitoring of a subject, and/or to obtain spatially relevant information from a plurality of locations on the subject during use.
  • One or more devices in the network may include a time synchronization service, the time synchronization service configurable so as to periodically align the local time sources of each device to those of each of the other devices in the network.
  • the time synchronization may be performed every second, every ten seconds, every thirty seconds, every minute, or the like.
  • one or more local devices may be coupled to an external time source such as an internet accessible time protocol, or a geolocation-based time source. Such information may be brought into the network so as to help align a global time reference for devices in the network. Such information may propagate through the network devices using the time synchronization service.
  • a carrying case of a rapid care delivery deployment kit may provide the time synchronization service for sensing devices and wireless gateways in a local monitoring environment.
  • one or more metrics measured from a subject in connection with one or more devices in the network may be time aligned with one or more metrics from a different subject in the network.
  • events that can simultaneously affect multiple subjects can be registered and higher level event classification algorithms are configured so as to generate an appropriate alert based on the metrics measured.
  • an event may include a loud audible event, or a physiological response to an event, the event classification algorithm is configured so as to increase the priority of an alert if the number of subjects affected by the event increases beyond a set number.
  • an event may be one or more temporally-triggered events, one or more spatially-triggered events, one or more occurrence-triggered events, a combination thereof, or the like.
  • the modules and/or patches may include corresponding interconnects for coupling with each other during use.
  • the interconnects may include one or more connectors, configured such that the modules and patches may only couple in a single unique orientation with respect to each other.
  • the modules may be color coded by function.
  • a temporary stiffening element attached to a patch may include instructions, corresponding color coding, etc., so as to assist a user or subject with simplifying the process of monitoring.
  • one or more patches and/or modules may be used to provide a stimulus to the subject, as will be described in further detail below.
  • a modular physiological monitoring system in accordance with the present disclosure to monitor a subject, to monitor an electrocardiogram (EKG) of a subject, to perform one or more tasks in accordance with the present disclosure, etc.
  • an interface e.g., a patch in accordance with the present disclosure
  • the interface or patch may include a substrate, an adhesive coupled to the substrate formulated for attachment to the skin of a subject, and one or more sensors and/or electrodes each in accordance with the present disclosure coupled to the substrate, arranged, configured, and dimensioned to interface with the subject.
  • the substrate may be formed from an elastic or polymeric material, such that the patch is configured to maintain operation when stretched to more than 25%, more than 50%, or more than 80%.
  • a system for measuring the effect of an impact on a physiological state of a subject including an electroencephalogram (EEG) device (e.g., a patch/module pair in accordance with the present disclosure configured to measure local electrophysiological signals associated with brain activity in adjacent tissues) in accordance with the present disclosure, configured for placement behind an ear, on the forehead, near a temple, onto the neck of the subject, or the like, the EEG device configured to measure an electroencephalographic signal from the head of the subject so as to produce an EEG signal, and configured to measure one or more kinetic and/or kinematic signals from the head of the subject so as to produce an impact signal, and a processor included in or coupled to the EEG device, the processor configured to receive the EEG signal, the impact signals, and/or signals generated therefrom, the processor including an algorithm, the algorithm configured to analyze the impact signals to determine if the subject has suffered an impact, to separate the signals into pre impact and post impact portions and to compare the pre and post impact portions of the EEG signal, to determine the effect
  • Adhesively applied stimulating devices may be provided as two components - a disposable body interface and a reusable component.
  • the disposable body interface may be applied so as to conform to the desired anatomy of the subject, and wrap around the body such that the reusable component may interface with the disposable component in a region that is open and free from a natural interface between the subject and another surface.
  • the modular physiological monitoring system 200 includes a sensing device 210, an accessory device 215 and a stimulating device 220 attached to a subject 201 that are in wireless communication 225 with a host device 230.
  • the host device 230 includes a processor, a memory and a network interface.
  • the accessory sensing device 215 is mounted on equipment carried by the subject 201, such as a firearm, firearm holster, etc.
  • the hots device 230 may be embodied as a wireless gateway and/or a carrying case of a rapid care delivery deployment kit.
  • the processor may comprise a microprocessor, a microcontroller, an applicationspecific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other type of processing circuitry, as well as portions or combinations of such circuitry elements.
  • ASIC applicationspecific integrated circuit
  • FPGA field-programmable gate array
  • the memory may comprise random access memory (RAM), read-only memory (ROM) or other types of memory, in any combination.
  • RAM random access memory
  • ROM read-only memory
  • the memory and other memories disclosed herein may be viewed as examples of what are more generally referred to as “processor-readable storage media” storing executable computer program code or other types of software programs.
  • Articles of manufacture comprising such processor-readable storage media are considered embodiments of the invention.
  • a given such article of manufacture may comprise, for example, a storage device such as a storage disk, a storage array or an integrated circuit containing memory.
  • the processor may load the computer program code from the memory and execute the code to provide the functionalities of the host device 230.
  • FIG. 2D shows a schematic diagram of aspects of the accessory device 215 in modular physiological monitoring system 200.
  • the accessory device 215 includes one or more of a processor, a memory device, a controller, a power supply, a power management and/or energy harvesting circuit, one or more peripherals, a clock, an antenna, a radio, a signal conditioning circuit, a driver, a stimulator, vital sign sensor(s), a sensor communication circuit, and secondary sensor(s).
  • the accessory device 215 is configured for wireless communication 225 with the sensing device 210, the stimulating device 220, and the host device 230.
  • FIGS. 3A-3F show a wearable sensor system 300 configured for monitoring physiologic, location, and contextual and/or environmental data for a plurality of users, and for analyzing such data for use in health monitoring, event detection, etc.
  • the wearable sensor system 300 provides the capability for assessing the condition of the human body of a plurality of users (e.g., including user 336 and a crowd of users 338).
  • the wearable sensor system 300 includes a wearable device 302 that is affixed to user 336, as well as one or more accessory devices 315 having sensors configured for capturing contextual and/or environmental information for the user 336 and/or the crowd of users 338.
  • the locally mobile and highly mobile users collectively act as human “shuttles” of data within and across compartments of the compartmentalized monitoring environment, via wearable devices and/or wireless gateways associated with the locally mobile and highly mobile users.
  • Such human shuttles communicate data until it reaches one or more designated compartmentalized network sink nodes, such as compartmentalized network sink node 390 that is capable of providing a data link to network 384 outside the compartmentalized monitoring environment.
  • the compartmentalized network sink node 390 may comprise a wearable device, a wireless gateway, or a smart device (e.g., a smartphone) which is associated with one of the user 336 or a user in the crowd of users 338 (e.g., potentially one of the locally mobile or highly mobile users).
  • the compartmentalized network sink node 390 may also or alternatively comprise a carrying case of a rapidly deployable physiologic monitoring kit, a deployable device housed in a housing of the carrying case of the rapidly deployable physiologic monitoring kit, etc.
  • a rapid care delivery deployment kit provides rapidly deployable physiologic monitoring capability “in a box” via a rapid deployment carrying case, which is a mobile case that may be brought to the local monitoring environment 301.
  • the rapid deployment carrying case includes a housing storing, among other things, various sensing devices (e.g., wearable device 302 and accessory devices 315) and wireless gateways (e.g., wireless gateway 340).
  • various sensing devices e.g., wearable device 302 and accessory devices 315
  • wireless gateways e.g., wireless gateway 340.
  • Each of the wireless gateways stored in the rapid deployment carrying case is assumed to be paired with a communications unit of the rapid deployment carrying case, and may be dynamically paired or otherwise associated with one of the user 336 or a user in the crowd of users 338.
  • Different ones of the sensing devices stored in the rapid deployment carrying case may be dynamically paired or associated with different ones of the wireless gateways.
  • the given sensing device Once a given sensing device is paired or associated with a given wireless gateway associated with a given one of the user 336 or a user in the crowd of users 338, the given sensing device may be attached to or otherwise placed in or near the given user to perform physiologic, contextual and/or environmental monitoring of the given user.
  • the wireless gateways which are associated with different ones of the user 336 and users in the crowd of users 338, relay physiologic, contextual and/or environmental monitoring data from its associated sensing devices to the rapid deployment carrying case in the local monitoring environment 301.
  • the compartmentalized monitoring environment 301 may comprise, for example, a hospital, a mobile device fleet, a ship, a submarine, an underground bunker, a manufacturing installation, or another environment which has no, poor or limited communications without outside external networks (e.g., the network 384, the Al wearable device network 348, the third-party networks 368).
  • Clusters in the compartmentalized monitoring environment 301 may be compartmentalized and disconnected. Bridging data between subjects (e.g., the user 336 and crowd of users 338) and network sinks or exit points (e.g., the compartmentalized network sink node 390) can be challenging and chaotic.
  • the network sinks or exit points (e.g., computing centers providing the network sinks or exit points) need to be in particular locations where not many of the users 336 or crowd of users 338 dwell or travel.
  • Illustrative embodiments provide approaches for managing data transfer in such challenging environments (e.g., the compartmentalized monitoring environment 301) by allowing mobile network nodes (e.g., wearable devices, wireless gateways, smart devices, etc.) which are connected with physical objects (e.g., humans such as one or more of the user 336 and crowd of users 338, robots, etc.) that move within the compartmentalized monitoring environment 301 to assist with data transfer across dead zones (e.g., across compartments of the compartmentalized monitoring environment 301).
  • mobile network nodes e.g., wearable devices, wireless gateways, smart devices, etc.
  • physical objects e.g., humans such as one or more of the user 336 and crowd of users 338, robots, etc.
  • the wireless gateway 340 has an internal processor and works together with other wireless gateways to get data to end users (e.g., smartphones of the end users), and back to network exit points such as the compartmentalized network sink node 390 (e.g., one of the wireless gateways, a smartphone, a computation unit of the rapid deployment carrying case, etc.).
  • end users e.g., smartphones of the end users
  • network exit points such as the compartmentalized network sink node 390 (e.g., one of the wireless gateways, a smartphone, a computation unit of the rapid deployment carrying case, etc.).
  • the mesh network may be formed on one or more physical layers, each physical layer comprising a range of frequency bands, upon which data can be transferred.
  • the mesh network may comprise at least one mesh protocol based on a long range evolution schema, an ultrawideband (UWB) mesh protocol, a Bluetooth Low Energy (BLE) mesh protocol, a WiFi mesh protocol, a HaLow mesh protocol, a private 5G mesh protocol, or the like.
  • UWB ultrawideband
  • BLE Bluetooth Low Energy
  • the mesh network of devices may be configured to use time of flight (TOF), time difference of arrival (TDoA), and phase difference of arrival (PDoA) technologies, or the like to enable extremely accurate (e.g., within a centimeter) distance and location tracking and two- way ranging capabilities between subjects in the network.
  • the smart devices may be equipped with applications (e.g., the Android Tactical Awareness Kit (ATAK), iTAK, Kill Switch, winTAK, or the like) in order to visualize such location-based relationships between the devices.
  • applications e.g., the Android Tactical Awareness Kit (ATAK), iTAK, Kill Switch, winTAK, or the like
  • a rapid care delivery deployment kit may include sensing devices (e.g., wearable physiologic sensors such as wearable device 302 and/or accessory devices such as accessory devices 315) for each of the users to be monitored (e.g., the user 336 and the crowd of users 338).
  • the rapid care delivery deployment kit may further include accompanying or integrated communication devices (e.g., PCDs, such as wireless gateway 340) for use in establishing a local network among the user 336, the crowd of users 338 and the compartmentalized network sink node 390.
  • sensing devices e.g., wearable physiologic sensors such as wearable device 302 and/or accessory devices such as accessory devices 315) for each of the users to be monitored (e.g., the user 336 and the crowd of users 338).
  • the rapid care delivery deployment kit may further include accompanying or integrated communication devices (e.g., PCDs, such as wireless gateway 340) for use in establishing a local network among the user 336, the crowd of users 338 and the compartmental
  • Each of the PCDs or wireless gateways may manage the BAN associated with a single one of the user 336 or a user in the crowd of users 338, with a local or mesh network being established between the compartmentalized network sink node 390 and the different PCDs or wireless gateways.
  • the compartmentalized network sink node 390 may also include: one or more processing devices or other compute hardware for generating or deriving reports or metrics from the physiologic, contextual and/or environmental data associated with the user 336 and the crowd of users 338; one or more data storage devices for storing the physiologic, contextual and/or environmental data associated with the user 336 and the crowd of users 338 (or reports or metrics which are derived or generated therefrom); communication hardware for offloading the physiologic, contextual and/or environmental data (or metrics or reports which are generated or derived therefrom) to the Al wearable device network 348 and/or the third-party networks 368; and a power supply for powering the compute hardware, the data storage devices, the communication hardware, etc.
  • the communication hardware provides a long-range communication system to provide a data link between the compartmentalized monitoring environment 301 and a remote site (e.g., the Al wearable device network 348, the third-party networks 368, etc.).
  • the remote site may include a hospital, a role 2 facility, a role 1 facility, a fire station, a command center, etc.
  • the compartmentalized network sink node 390 may, in some cases, include sensing devices embodied as patch-module sets, wireless gateways, smart devices (e.g., smartphones, tablets, etc.), one or more displays, compute hardware, a radio system or other communication hardware, a mass storage drive or other data storage devices, etc.
  • the network 384 may comprise a physical connection (wired or wireless), the Internet, a cloud communication network, etc.
  • wireless communication networks that may be utilized include networks that utilize Visible Light Communication (VLC), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE), Wireless Local Area Network (WLAN), Infrared (IR) communication, Public Switched Telephone Network (PSTN), Radio waves, and other communication techniques known in the art.
  • VLC Visible Light Communication
  • WiMAX Worldwide Interoperability for Microwave Access
  • LTE Long Term Evolution
  • WLAN Wireless Local Area Network
  • IR Infrared
  • PSTN Public Switched Telephone Network
  • Radio waves and other communication techniques known in the art.
  • the local monitoring environment 301 is connected to the network 384 via the rapid deployment carrying case 390.
  • the Al wearable device network 348 and a verification entity 386 coupled to the third-party networks 368.
  • FIGS. 3B-3F Detailed views of the wearable device 302, wireless gateway 340, Al
  • the wearable device 302 is implemented using one or more patch-module sets as described above with respect to FIGS. 1 and 2A-2C.
  • the patch-module sets described above with respect to FIGS. 1 and 2A-2C are just one example of wearable technology that may be used to provide the wearable device 302.
  • Various other types of wearable technology may be used to provide the wearable device in other embodiments, including but not limited to wearables, fashion technology, tech togs and other types of fashion electronics that include “smart” electronic devices (e.g., electronic devices with micro- controllers) that can be incorporated into clothing or worn on the body as implants or accessories.
  • Wearable devices such as activity trackers are examples of Internet of Things (loT) devices, and such “things” include electronics, software, sensors and connectivity units that are effectors enabling objects to exchange data (including data quality) through the Internet with a manufacturer, operator and/or other connected devices without requiring human intervention.
  • Wearable technology has a variety of applications, which grows as the field itself expands. Wearable technology appears prominently in consumer electronics with the popularization of smartwatches and activity trackers. Apart from commercial uses, wearable technology is being incorporated into navigation systems, advanced textiles, and health care.
  • the wearable device 302 is capable of detecting and collecting medical data (e.g., body temperature, respiration, heart rate, etc.) from the wearer (e.g., user 336).
  • the wearable device 302 can remotely collect and transmit real-time physiological data to health care providers and other caretakers responsible for ensuring their communities stay healthy, via the wireless gateway 340 and the compartmentalized network sink node case 390.
  • the wearable sensor system 300 in some embodiments, is user-friendly, hypoallergenic, unobtrusive, and cost-effective.
  • the wearable sensor system 300 is configured to transmit data directly into existing health informatics and health care management systems from the comfort of patients’ homes or in other remote environments (e.g., the local monitoring environment 301) outside health care facilities.
  • the wearable device 302 is designed to monitor the cardiopulmonary state of a subject (e.g., user 336) over time in home or in clinical settings.
  • Onboard sensors of the wearable device 302 can quantitatively detect and track severity of a variety of disease symptoms including fever, coughing, sneezing, vomiting, infirmity, tremor, and dizziness, as well as signs of decreased physical performance and changes in respiratory rate/depth.
  • the wearable device 302 may also have the capability to monitor blood oxygenation.
  • the wearable device 302 collects physiologic monitoring data from the subject user 336 utilizing a combination of a disposable sampling unit 312 and a reusable sensing unit 314 shown in FIG. 3B.
  • the patch-module sets described above with respect to FIGS. 1 and 2A-2C are an example implementation of the disposable sampling unit 312 and reusable sensing unit 314.
  • the disposable sampling unit 312 may be formed from a softer-than-skin patch.
  • the wearable device 302, formed from the combination of the disposable sampling unit 312 and reusable sensing unit 314, is illustratively robust enough for military use, yet extremely thin and lightweight.
  • the disposable sampling unit 312 and reusable sensing unit 314 may collectively weigh less than 0.1 ounce, about the same as a U.S. penny.
  • the wearable device 302 may be adapted for placement almost anywhere on the body of the user 336, such as the various placement sites shown in FIG. 1 and described above.
  • the wearable device 302 may include a number of other components as illustrated in FIG. 3B.
  • Such components include a power source 304, a communications unit 306, a processor 308, a memory 310, a GPS unit 330, an UWB communication unit 332, contextual analysis module 334, sensor data reconstruction module 335, and compartmentalized network shuttling module 339.
  • the power source or component 304 of the wearable device 302 includes one or more modules with each module including a power source (e.g., a battery, a rechargeable battery, an energy harvesting transducer, a microcircuit, an energy reservoir, a thermal gradient harvesting transducer, a kinetic energy harvesting transducer, a radio frequency energy harvesting transducer, a fuel cell, a biofuel cell, combinations thereof, etc.).
  • a power source e.g., a battery, a rechargeable battery, an energy harvesting transducer, a microcircuit, an energy reservoir, a thermal gradient harvesting transducer, a kinetic energy harvesting transducer, a radio frequency energy harvesting transducer, a fuel cell, a biofuel cell, combinations thereof, etc.
  • the communications unit 306 of the wearable device 302 may be embodied as communication circuitry, or any communication hardware that is capable of transmitting an analog or digital signal over one or more wired or wireless interfaces.
  • the communications unit 306 includes transceivers or other hardware for communications protocols, such as Near Field Communication (NFC), WiFi, Bluetooth, infrared (IR), modem, cellular, ZigBee, a Body Area Network (BAN), and other types of wireless communications.
  • the communications unit 306 may also or alternatively include wired communication hardware, such as one or more universal serial bus (USB) interfaces.
  • USB universal serial bus
  • the processor 308 of the wearable device 302 is configured to decode and execute any instructions received from one or more other electronic devices and/or servers.
  • the processor 308 may include any combination of one or more general-purpose processors (e.g., Intel® or Advanced Micro Devices (AMD)® microprocessors), one or more special-purpose processors (e.g., digital signal processors or Xilink® system on chip (SOC) field programmable gate array (FPGA) processors, application-specific integrated circuits (ASICs), etc.), cloudbased processing units (e.g., Amazon Web Services (AWS) processing units), edge computing systems, embedded systems (e.g., Nvidia® JetsonTM), etc.
  • general-purpose processors e.g., Intel® or Advanced Micro Devices (AMD)® microprocessors
  • special-purpose processors e.g., digital signal processors or Xilink® system on chip (SOC) field programmable gate array (FPGA) processors, application-specific integrated circuits (
  • the processor 308 is configured in some embodiments to execute one or more computer-readable program instructions, such as program instructions to carry out any of the functions described herein including but not limited to those of the contextual analysis module 334, the sensor data reconstruction module 335 and the compartmentalized network shuttling module 339 described below.
  • the processor 308 is illustratively coupled to the memory 310, with the memory 310 storing such computer-readable program instructions.
  • the memory 310 may include, but is not limited to, fixed hard disk drives, magnetic tape, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs), magnetooptical disks, semiconductor memories such as read-only memory (ROM), random-access memory (RAM), programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), flash memory, magnetic or optical cards, or other type of media/machine-readable medium suitable for storing electronic instructions.
  • the memory 310 may comprise modules implemented as one or more programs.
  • a non- transitory processor-readable storage medium has stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device (e.g., the processor 308) causes said at least one processing device to perform one or more aspects of the methods, algorithms and process flows described herein.
  • the processor 308 and memory 310 are an example of a processing device or controller.
  • the controller may comprise a central processing unit (CPU) for carrying out instructions of one or more computer programs for performing arithmetic, logic, control and input/output (I/O) operations specified by the instructions (e.g., as specified by the contextual analysis module 334 and/or the sensor data reconstruction module 339 as described in further detail below).
  • Such computer programs may be stored in the memory 310.
  • the memory 310 provides electronic circuitry configured to temporarily store data that is utilized by the processor 308. In some embodiments, the memory 310 further provides persistent storage for storing data utilized by the processor 308.
  • other components of the wearable sensor system 300 may also include one or more processors coupled to one or more memories providing processing devices implementing the functionality of such components.
  • the wearable device 302 illustratively includes the disposable sampling unit 312 which may be embodied as a physical interface to the skin of the user 336. Patches as described elsewhere herein are examples of a disposable sampling unit 312. Such patches are adapted for attachment to a human or animal body (e.g., attachable to the skin thereof, reversibly attachable, adhesively attachable, with a disposable interface that couples to a reusable module, etc.).
  • the disposable sampling unit 312 is part of a system that is capable of modular design, such that various wearable devices or portions thereof (e.g., reusable sensing unit 314) are compatible with various disposable sampling units with differing capabilities.
  • the patch or more generally the disposable sampling unit 312 allows sterile contact between the user 336 and other portions of the wearable device 302, such as the reusable sensing unit 314.
  • the other portions of the wearable device 302 e.g., which may be embodied as a module as described above with respect to FIGS. 1 and 2A-2C
  • the patch or other disposable sampling unit 312 is suitable for wearing over a duration of time in which the user 336 is undergoing physiological monitoring.
  • the patch or disposable sampling unit 312 may be disposed of after the monitoring duration has ended.
  • the reusable sensing unit 314 includes various sensors, such as one or more temperature sensors 316, one or more heart rate sensors 318, one or more respiration sensors 320, one or more pulse oximetry sensors 322, one or more accelerometer sensors 324, one or more gyroscope sensors 326, one or more audio sensors 328, and one or more other sensors 329.
  • sensors such as one or more temperature sensors 316, one or more heart rate sensors 318, one or more respiration sensors 320, one or more pulse oximetry sensors 322, one or more accelerometer sensors 324, one or more gyroscope sensors 326, one or more audio sensors 328, and one or more other sensors 329.
  • One or more of the sensors 316-329 may be embodied as electric features, capacitive elements, resistive elements, touch sensitive components, analyte sensing elements, printed electrochemical sensors, light sensitive sensing elements, electrodes (e.g., including but not limited to needle electrodes, ionically conducting electrodes, reference electrodes, etc.), electrical traces and/or interconnects, stretch sensing elements, contact interfaces, conduits, microfluidic channels, antennas, stretch resistant features, stretch vulnerable features (e.g., a feature that changes properties reversibly or irreversibly with stretch), strain sensing elements, photo-emitters, photodiodes, biasing features, bumps, touch sensors, pressure sensing elements, interfacial pressure sensing elements, piezoelectric elements, piezoresistive elements, chemical sensing elements, electrochemical cells, electrochemical sensors, redox reactive sensing electrodes, light sensitive structures, moisture sensitive structures, pressure sensitive structures, magnetic structures, bioadhesives, antennas, transistors, integrated circuits, transce
  • one or more of the sensors 316-329 have a controlled mass transfer property, such as a controlled moisture vapor conductivity so as to allow for a differential heat flux measurement through the patch or other disposable sampling unit 312. Such properties of one or more of the sensors 316-329 may be used in conjunction with the one or more temperature sensors 316 to obtain core temperature measurements of the user 336. It should be noted that one or more of the sensors 316-328 or the sensing unit 314 generally may be associated with signal conditioning circuitry used in obtaining core temperature or other measurements of physiologic parameters of the user 336.
  • Core temperature measurements may, in some embodiments, be based at least in part on correlation parameters extracted from sensors of multiple wearable devices, or from sensors of the same wearable device that interface with different portions of the user 336.
  • the correlation parameters may be based on thermal gradients computed as comparisons of multiple sensor readings (e.g., from a first subset of sensors oriented to make thermal contact with the user 336 and from a second subset of sensors oriented to make thermal contact with ambient surroundings, etc.). Core temperature readings may thus be estimated from the thermal gradients.
  • Changes in core temperature readings from multiple sensor readings over some designated period of time are analyzed to generate correlation parameters that relate changes in core temperature readings from the multiple sensors.
  • this analysis includes determining which of the multiple sensors has a lowest thermal gradient and weighting the correlation parameters to the sensor or device having the lowest thermal gradient.
  • the temperature sensors 316 comprise one or more digital infrared temperature sensors (e.g., Texas Instruments TMP006 sensors).
  • the heart rate sensors 318 are configured to sense physiological parameters of the user 336, such as conditions of the cardiovascular system of the user 336 (e.g., heart rate, blood pressure, heart rate variability, etc.).
  • the physiological parameters comprise one or more bioimpedance measurements
  • correlation parameters may be generated by extracting local measures of water content from bioimpedance signals recorded from multiple sensors potentially at different sites on the body of the user 336.
  • the local measures of water content recorded by different devices or sensors may be recorded during at least a portion of a transitionary period as described above to generate correlation parameters for application to bioimpedance signals recorded by the different sensors to offset at least a portion of identified differences therebetween.
  • the correlated changes in the local measures of water content may be associated with a series of postural changes by the user 336.
  • the respiration sensors 320 are configured to monitor the condition of respiration, rate of respiration, depth of respiration, and other aspects of the respiration of the user 336.
  • the respiration sensors 320 may obtain such physiological parameters by placing the wearable device 302 (e.g., a patch-module pair thereof) on the abdomen of the user 336 for monitoring movement or breathing, below the rib cage for monitoring respiration (generally on the right side of the body to substantially reduce EKG influences on the measurements), such placement enabling the respiration sensors 320 to provide rich data for respiration health, which may be advantageous in detection of certain infectious diseases that affect the respiratory tract of victims, such as, for example, coronavirus/COVID-19.
  • the pulse oximetry sensors 322 are configured to determine oxygen saturation (SpO2) using a pulse oximeter to measure the oxygen level or oxygen saturation of the blood of the user 336.
  • the accelerometer sensors 324 are configured to measure acceleration of the user 336.
  • Single and multi -axis models of accelerometers may be used to detect the magnitude and direction of the proper acceleration as a vector quantity, and can be used to sense orientation (e.g., based on the direction of weight changes), coordinate acceleration, vibration, shock, and falling in a resistive medium (e.g., a case where the proper acceleration changes, since it starts at zero then increases).
  • the accelerometer sensors 324 may be embodied as micromachined microelectromechanical systems (MEMS) accelerometers present in portable electronic devices such as the wearable device 302.
  • MEMS micromachined microelectromechanical systems
  • the accelerometer sensors 324 may also be used for sensing muscle contraction for various activities, such as running and other erect sports.
  • the accelerometer sensors 324 may detect such activity by measuring the body or extremity center of mass of the user 336. In some cases, the body center of mass may yield the best timing for the injection of fluid. Embodiments, however, are not limited solely to use with measuring the body center of mass.
  • the gyroscope sensors 326 are configured to measure orientation and angular velocity, and may be used to detect deviation of an object (e.g., the sensing unit 314, the wearable device 302) from a desired orientation.
  • the audio sensors 328 are configured to convert sound into electrical signals, and may be embodied as one or more microphones or piezoelectric sensors that use the piezoelectric effect to measure changes in pressure, acceleration, temperature, strain, or force by converting them to an electrical charge.
  • the audio sensors 328 may include ultrasonic transducer receivers capable of converting ultrasound into electrical signals.
  • the sensors 316-328 described above are presented by way of example only, and that the sensing unit 314 may utilize various other types of sensors 329 as described elsewhere herein.
  • the other sensors 329 include one or more of motion sensors, humidity sensors, cameras, radiofrequency receivers, thermal imagers, radar devices, lidar devices, ultrasound devices, speakers, etc.
  • the GPS unit 330 is a component of the wearable device 302 configured to detect global position using GPS, a satellite-based radio navigation system owned by the U.S. government and operated by the U.S. Space Force.
  • GPS is one type of global navigation satellite system (GNSS) that provides geolocation and time information to a GPS receiver anywhere on or near the Earth where there is an unobstructed line of sight to four or more GPS satellites.
  • GNSS global navigation satellite system
  • the UWB communication unit 332 is a component of the wearable device 302 configured to detect UWB radiofrequencies.
  • UWB is a short-range, wireless communication protocol similar to Bluetooth or WiFi, which uses radio waves at a very high frequency.
  • UWB also uses a wide spectrum of several gigahertz (GHz).
  • the functioning of a UWB sensor is to provide the ability to continuously scan an entire room and provide spatial awareness data to the wearable device 302, improving the localization of the wearable device 302 particularly in conjunction with use of the GPS unit 330.
  • the contextual analysis module 334 is configured to execute various functionality for combining sensor data from the sensing unit 314 (e.g., physiologic monitoring data for the user 336) along with sensor data from the accessory devices 315 (e.g., contextual and/or environmental information associated with the user 336 and/or the crowd of users 338) for higher-level analysis.
  • sensor data from the sensing unit 314 e.g., physiologic monitoring data for the user 336
  • accessory devices 315 e.g., contextual and/or environmental information associated with the user 336 and/or the crowd of users 33
  • the sensor data reconstruction module 335 is configured to execute various functionality for reconstructing sensor data from the sensing unit 314 and/or the accessory devices 315 (e.g., to correct for missing, erroneous, corrupt or contaminated data, to account for sensors of the sensing unit 314 and/or the accessory devices 315 which have been destroyed, disabled or are otherwise unable to produce sensor data, to account for sensors of the sensing unit 314 and/or the accessory devices 315 which are inherently limited in what data the can produce and convey, etc.).
  • various functionality for reconstructing sensor data from the sensing unit 314 and/or the accessory devices 315 e.g., to correct for missing, erroneous, corrupt or contaminated data, to account for sensors of the sensing unit 314 and/or the accessory devices 315 which have been destroyed, disabled or are otherwise unable to produce sensor data, to account for sensors of the sensing unit 314 and/or the accessory devices 315 which are inherently limited in what data the can produce and convey, etc.
  • the compartmentalized network shuttling module 339 is configured to execute various functionality for communicating data within and across compartments of the compartmentalized monitoring environment 301.
  • the user 336 may be a human or animal to which the wearable device 302 is attached.
  • Sensor data and localization data collected by the wearable device 302, along with contextual and/or environmental data collected from the accessory devices 315, may be provided to Al wearable device network 348 for analysis, with portions or such analysis being provided to one or more of the third-party networks 368 for various purposes.
  • Communication of the sensor and localization data from the wearable device 302, as well as communication of the contextual and/or environmental data from the accessory devices 315, to the Al wearable device network 348 may take place via the wireless gateway 340, with the communication between the wireless gateway 340 and the Al wearable device network 348 taking place over one or more networks 384 using the compartmentalized network sink node 390.
  • the user 336 may configure the wireless gateway 340 to include a user profile 344.
  • the user profile 344 may include various health and physiological data about the user 336 that may not be obtained by sensors 316-329 of the wearable device 302.
  • the user profile 344 may include information such as a name (e.g., first, last and middle name), biological sex, age (e.g., in years), weight (e.g., in pounds, kilograms, etc.), and height (e.g., in feet or inches, in meters, etc.).
  • the user profile 344 may also include known diseases and disorders (e.g., asthma, allergies, current medications, family medical history, other medical data, etc.), where such information may include Protected Health Information (PHI) regulated by American Health Insurance Portability and Accountability Act (HIPAA) or other applicable rules and regulations.
  • PHI Protected Health Information
  • HIPAA American Health Insurance Portability and Accountability Act
  • PHI includes individually identifiable health information that relates to one or more of the past, present, or future physical or mental health or condition of an individual; provision of health care to the individual by a covered entity (e.g., a hospital or doctor); the past, present, or future payment for the provision of health care to the individual; telephone numbers, fax numbers, email addresses, Social Security numbers, medical record numbers, health plan beneficiary numbers, license plate numbers, uniform resource locators (URLs), full-face photographic images or any other unique identifying numbers, characteristics, codes, or combination thereof that allows identification of an individual.
  • a covered entity e.g., a hospital or doctor
  • the user profile 344 may further include an emergency contact (e.g., name, phone number, address, etc.), next of kin (e.g., name, phone number, address, etc.), preferred hospital (e.g., name, phone number, address, etc.) and primary care physician (PCP) of the user 336 (e.g., name, phone number, place of business, etc.).
  • the user profile 344 may further include local caregiver information (e.g., name, phone number, address, etc.) and preferred first responder network information (e.g., name, phone number, address, etc.).
  • the local caregiver may be, for example, a nursing agency, a private caregiver such as a family member, a nursing home, or other local caregivers such as physical therapists, chiropractors, pharmacists, pediatricians, acupuncture specialists, massage therapists, etc.
  • the local caregiver is associated with one or more telemedicine networks.
  • the preferred first responder network may be, for example, a local hospital and/or a local ambulatory rescue agency.
  • the preferred first responder network may be an interface with an emergency calling network (e.g., 911).
  • Such network interfaces may support not only communication with the Al wearable device network 348 over network 384 (e.g., utilizing the compartmentalized network sink node 390), but also communications between the wearable device 302 and the wireless gateway 340.
  • Any combination of network types may be utilized, including but not limited to UWB, NFC, WiFi, Bluetooth, BLE, IR, modem, cellular, ZigBee, BAN, etc.
  • the wireless gateway 340 may also be provisioned with contextual analysis module 345, sensor data reconstruction module 347 and compartmentalized network shuttling module 349, which provide functionality similar to that of the contextual analysis module 334, the sensor data reconstruction module 335 and the compartmentalized network shuttling module 339, respectively.
  • the wireless gateway 340 may be, for example, a smartphone, a tablet, a laptop or desktop computer, an Internet-connected modem, a wireless router or standalone wireless hub device connected to the Internet, etc.
  • the wireless gateway 340 may itself comprise or be incorporated into one or more wearable devices (e.g., a smartwatch, an activity tracker, etc.). In some cases, the wireless gateway 340 may be part of the wearable device 302, or vice versa.
  • the wireless gateway 340 is illustratively a smart device that is uniquely paired with the user 336, and which allows rapid onboarding of wearable devices such as wearable device 302 to a local BAN associated with the user 336.
  • the wireless gateway 340 may act as a bridge between the BAN of the user 336 and a mesh network of the compartmentalized monitoring environment 301 formed by the wireless gateway 340 and other wireless gateways associated with one or more of the users in the crowd of users 338.
  • the wireless gateway 340 may also be paired with the compartmentalized network sink node 390 and be configured for communication therewith.
  • the wireless gateway 340 includes a wearable device module 342 and accessory device module 343 that provide software programs or computer instructions for providing various functionality of the wireless gateway 340.
  • the wireless gateway 340 is assumed to comprise at least one processing device or controller including a processor coupled to a memory for executing the functionality of the wearable device module 342, the accessory device module 343, the contextual analysis module 345, the sensor data reconstruction module 347 and the compartmentalized network shuttling module 349.
  • Such functionality may include, for example, wirelessly pairing the wearable device 302 and one or more of the accessory devices 315 in a BAN associated with the user 336.
  • Such functionality may also include receiving the sensor data and the localization data from the wearable device 302 and the contextual and/or environmental data from the accessory devices 315 via the communications unit 346, and possibly performing a preliminary analysis of the sensor data, the localization data and the contextual and/or environmental data. Such analysis may be based at least in part on information stored in the user profile 344. Based on such analysis, the wearable device module 342 and the accessory device module 343 may determine whether any immediate notifications should be provided to the user 336. Such notifications may comprise, for example, indications of symptoms associated with at least one disease state. In other embodiments, the wearable device 302 functions as a pass-through entity and does not perform such preliminary analysis.
  • the wireless gateway 340 may provide the sensor data and the localization data received from the wearable device 302, along with the associated user profile 344 and the contextual and/or environmental data obtained from the accessory devices 315, to the compartmentalized network sink node 390 and/or the Al wearable device network 348 over network 384 as a pass-through entity.
  • the wearable device module 342 and the accessory device module 343 of the wireless gateway 340 may receive any combination of diagnostic information, world health information, sensor data analysis, localization analysis, analysis created from a fusion of data from a plurality of sensors from the Al wearable device network 348, etc. At least a portion of the received information is based on analysis of the sensor data, the localization data, the user profile 344, the contextual and/or environmental data, or information derived therefrom previously provided by the wireless gateway 340 to the compartmentalized network sink node 390 and/or the Al wearable device network 348. At least a portion of the received information is used to generate notifications or other output via a graphical user interface (GUI) of the wireless gateway 340, the wearable device 302, one or more of the accessory devices 315, or another type of local or remote indicator device.
  • GUI graphical user interface
  • the wearable device module 342 and/or the accessory device module 343 may provide functionality for determining notification settings associated with the user 336, and to execute or deliver notifications in accordance with the determined notification settings utilizing the wearable device 302 and/or one or more of the accessory devices 315 or other devices.
  • the notification settings may specify the types of indicator devices that are part of or otherwise accessible to the wearable device 302 and/or the accessory devices 315 for delivering notifications to the user 336 (or to a doctor, nurse, physical therapist, medical assistant, caregiver, etc. associated with the user 336).
  • the indicator devices in some embodiments may be configured to deliver visual or audible alarms. In other embodiments, the indicator devices may be configured to provide stimulus or feedback via stimulating devices as described elsewhere herein.
  • Such stimulus or feedback may include physical stimulus (e.g., electrical, thermal, vibrational, pressure, stroking, a combination thereof, or the like), optical stimulus, acoustic stimulus, etc.
  • notifications may be delivered to remote terminals or devices other than the wearable device 302 and/or the accessory devices 315 associated with user 336. For example, notifications may be delivered to one or more devices associated with a doctor, nurse, physical therapist, medical assistant, caregiver, etc. associated with the user 336.
  • the notification delivery method may also or alternatively comprise a visual or audible read-out or alert from a “local” device that is in communication with the wearable device 302.
  • the local device may comprise, for example, a mobile computing device such as a smartphone, tablet, laptop etc., or another computing device, that is associated with the user 336.
  • the wearable device 302 is one example of a local device.
  • a local device may also include devices connected to the wearable device 302 via a BAN or other type of local or short- range wireless network (e.g., a Bluetooth network connection).
  • the notification delivery method may further or alternatively comprise a visual or audible read-out or alert from a “remote” device that is in communication with the wearable device 302 or the wireless gateway 340 via network 384, such as one or more of the accessory devices 315.
  • the remote device may be a mobile computing device such as a smartphone, tablet, laptop, etc., or another computing device (e.g., a telemetry center or unit within a hospital or other facility), that is associated with a doctor, nurse, physical therapist, medical assistant, caregiver, etc. monitoring the user 336. It should be understood that the term “remote” in this context does not necessarily indicate any particular physical distance from the user 336.
  • a remote device to which notifications are delivered may be in the same room as the user 336.
  • the term “remote” in this context is instead used to distinguish from “local” devices (e.g., in that a “local” device in some embodiments is assumed to be owned by, under the control of, or otherwise associated with the user 336, while a “remote” device is assumed to be owned by, under the control of, or otherwise associated with a user or users other than the user 336 such as a doctor, nurse, physical therapist, medical assistance, caregiver, first responder, squad leader, etc.).
  • the indicator devices may include various types of devices for delivering notifications to the user 336 (or to a doctor, nurse, physical therapist, medical assistant, caregiver, first responder, squad leader, etc. associated with the user 336).
  • one or more of the indicator devices comprise one or more light emitting diodes (LEDs), a liquid crystal display (LCD), a buzzer, a speaker, a bell, etc., for delivering one or more visible or audible notifications.
  • the indicator devices may include any type of stimulating device as described herein which may be used to deliver notifications to the user 336 (or to a doctor, nurse, physical therapist, medical assistant, caregiver, first responder, squad leader, etc. associated with the user 336).
  • FIG. 3A also shows the crowd of users 338, each of which is assumed to provide sensor data and localization data obtained by a plurality of wearable devices and/or accessory devices to the compartmentalized network sink node 390 and/or the Al wearable device network 348, possibly via respective wireless gateways.
  • the wearable devices, accessory devices and wireless gateways for the crowd of users 338 may be configured in a manner similar to that described herein with respect to the wearable device 302, the accessory devices 315, and the wireless gateway 340 associated with the user 336.
  • the wearable device 302 may be configured with multiple different types of sensors 316-329, it is generally not possible to configure a wearable device with every possible sensor that may be needed in different scenarios.
  • wearable devices are advantageously designed for comfortable wear and use, and thus may require a small form factor which cannot accommodate the possible range of sensors and sensor types which may be needed in different scenarios.
  • some types of sensors are large, heavy and/or expensive, and thus are not conducive to being incorporated as part of a wearable device. Nonetheless, different tasks may benefit from the use of contextual and/or environmental information which may be provided using sensor types that are not available in the wearable device 302.
  • the compartmentalized monitoring environment 301 where the user 336 is located is one with possible radiation exposure where dosimeter sensors would be advantageous (e.g., for correlating changes in physiologic parameters obtained from the sensing unit 314 of the wearable device 302 with knowledge of an amount and/or type of radiation that the user 336 is exposed to).
  • the wearable device 302 may not be configured with dosimeter sensors, as this may not be practical (e.g., due to the size, power, material and other requirements) or such a potential use case is not expected to come up very often.
  • accessory sensing devices 315 that include dosimeter sensors may be leveraged to provide such information which is used for contextual analysis (e.g., implemented by the contextual analysis module 334 on the wearable device 302, on the contextual analysis module 345 of the wireless gateway 340, on a contextual analysis module 365 of the Al wearable device network 348, on contextual analysis modules implemented by the accessory devices 315 and/or one or more of the third-party networks 368, etc.).
  • contextual analysis e.g., implemented by the contextual analysis module 334 on the wearable device 302, on the contextual analysis module 345 of the wireless gateway 340, on a contextual analysis module 365 of the Al wearable device network 348, on contextual analysis modules implemented by the accessory devices 315 and/or one or more of the third-party networks 368, etc.
  • the accessory sensing devices 315 may also or alternatively be used to determine the user 336’ s microenvironmental exposure to light, noise, temperature, humidity, pressure, etc. These and other factors can influence different aspects of the microenvironment of the user 336 which can be correlated with physiologic data obtained from the user 336 via the sensing unit 314 of the wearable device 302. This may include use cases such as impact/fall detection, detecting fatigue of the user 336, etc. Another use case is in determining a “wet-bulb” temperature of the user 336. The wet-bulb temperature of the user 336, which may be determined from microenvironmental monitoring of information such as light, temperature, humidity and pressure, can be correlated with measured physiologic data to determine harmful and potentially life-threatening conditions.
  • the microenvironmental monitoring of the wet-bulb temperature (e.g., via accessory devices 315) may be correlated with physiologic data measured from on-body sensors (e.g., from the sensing unit 314 of the wearable device 302) which characterize, for example, physical activity or exertion. This may be used to provide feedback to the user 336 (e.g., to stop the physical activity or exertion, to remove MOPP gear or other bulky equipment or clothing, etc.).
  • the microenvironmental monitoring of wet-bulb temperature may be correlated with local weather report information as well as measured physiologic data of the user 336 (e.g., to detect risk of heat exhaustion or other conditions).
  • the microenvironmental monitoring may also or alternatively utilize microenvironmental noise information to detect exposure to potentially harmful noise levels. This may include monitoring and detecting a microenvironmental infrasound signature, which may be correlated with physiologic data from the user 336 to characterize effects such as nausea, vomiting internal injuries (e.g., organ tearing), etc.
  • Noise exposure information may also be used to detect microenvironmental sound signatures (e.g., to detect exposure to radiofrequency (RF), to detect drones or vehicles in the area, to detect exposure to shots fired/explosions, to detect sounds indicative of coughing, vomiting or choking events, etc.) which may be time-correlated with physiologic data from the user 336 (e.g., core vital signs indicative of being hit by a shot fired, having injuries related to a blast exposure, being sick from dehydration, vomiting, choking, etc.).
  • RF radiofrequency
  • the microenvironmental information and physiologic monitoring data may be used for various types of contextual analysis, where the microenvironmental information and physiologic monitoring data are correlated with knowledge of what the user 336 is doing (e.g., whether the user 336 is awake or asleep, a physical workload or profile of the user 336, etc.).
  • Noise information in some cases, may be used for contextual analysis of the activity of multiple users (e.g., the user 336 and one or more of the users in the crowd of users 338) to provide spatial reference information (e.g., detecting where shots/blasts come from, where drones or vehicles are traveling, etc.).
  • the contextual analysis includes “friend/foe” detection, where the user 336 has a specific profile (e.g., ECG signature, tone/audio signature, etc.) which may be used to detect when the wearable device 302 associated with the user 336 is being utilized by another user (e.g., a potential “foe”).
  • a specific profile e.g., ECG signature, tone/audio signature, etc.
  • the accessory sensing devices 315 are leveraged to provide contextual and/or environmental information which is difficult, not possible or not practical to obtain utilizing the wearable device 302 alone. This may be due to the contextual and/or environmental information only being needed in limited use cases, such that the cost of implementing the required sensor types within the wearable device 302 is not practical or cost- effective.
  • the sensor types of the accessory devices 315 which are leveraged to obtain contextual and/or environmental information are not limited solely to sensor types which are difficult to implement within the small form factor or other constraints of the wearable device 302 (e.g., comfortable long-term wear by the user 336, cost, etc.).
  • the contextual and/or environmental information may be used in sensor data reconstruction algorithms implemented by one or more of the sensor data reconstruction module 335, the sensor data reconstruction module 347, and/or sensor data reconstruction module 367.
  • the Al wearable device network 348 is configured to receive data (e.g., sensor data and localization data from the wearable device 302, contextual and/or environmental data from the accessory devices 315, user profile 344, preliminary analysis of the sensor, localization and contextual and/or environmental data, etc.) from the compartmentalized network sink node 390 (which may be the wireless gateway 340 or a smart device associated with the user 336 or other wireless gateways or smart devices associated with users in the crowd of users 338).
  • the Al wearable device network 348 analyzes the received data using various software modules implementing Al algorithms for determining disease states, types of symptoms, risk of infection, contact between users, condition of physiological parameters, occurrence of events, event classification (e.g., including detection of firearm discharges), etc.
  • such modules include a third-party application programming interface (API) module 350, a pandemic response module 352, a vital monitoring module 354, a location tracking module 356, an automated contact tracing module 358, a disease progression module 360, an in-home module 362, an essential workforce module 364, a military or other security module 365, a contextual analysis module 365, a sensor data reconstruction module 367, and a compartmentalized network shuttling module 369.
  • the contextual analysis module 365, the sensor data reconstruction module 367, and the compartmentalized network shuttling module 369 are configured to provide functionality similar to that of the contextual analysis module 334, the sensor data reconstruction module 335, and the compartmentalized network shuttling module 339, respectively.
  • the Al wearable device network 348 also includes a database 366 configured to store the received data, results of analysis on the received data, data obtained from third-party networks 368, etc.
  • the Al wearable device network 348 is implemented as an application or applications running on one or more physical or virtual computing resources.
  • Physical computing resources include, but are not limited to, smartphones, laptops, tablets, desktops, wearable computing devices, servers, etc.
  • Virtual computing resources include, but are not limited to, VMs, software containers, etc.
  • the physical and/or virtual computing resources implementing the Al wearable device network 348, or portions thereof, may be part of a cloud computing platform.
  • a cloud computing platform includes one or more clouds providing a scalable network of computing resources (e.g., including one or more servers and databases).
  • the clouds of the cloud computing platform implementing the Al wearable device network 348 are accessible via the Internet over network 384.
  • the clouds of the cloud computing platform implementing the Al wearable device network 348 may be private clouds where access is restricted (e.g., such as to one or more credentialed medical professionals or other authorized users).
  • the Al wearable device network 348 may be considered as forming part of an emergency health network comprising at least one server and at least one database (e.g., the database 366) storing health data pertaining to a plurality of users (e.g., the user 336 and crowd of users 338).
  • the database 366 provides a data store for information about patient conditions (e.g., information about the user 336 and crowd of users 338), information relating to diseases including epidemics or pandemics, information related to firearm discharges and the context in which such firearm discharges occurred, etc. Although shown as being implemented internal to the Al wearable device network 348 in FIG. 3D, it should be appreciated that the database 366 may also be implemented at least in part external to the Al wearable device network 348 (e.g., as a standalone server or storage system). The database 366 may be implemented as part of the same cloud computing platform that implements the Al wearable device network 348. [00207] The Al wearable device network 348 may exchange various information with third- party networks 368. As shown in FIG.
  • the third-party networks 368 may include any combination of one or more first responder networks 370, one or more essential workforce networks 372, one or more local caregiver networks 374, one or more hospital networks 376, one or more state and local health networks 378, one or more federal health networks 380, one or more world health networks 382, one or more military or other security networks 383, etc.
  • Third-party networks 368 may also include telemedicine networks.
  • one or more of the local caregiver networks 374 may comprise or be associated with one or more telemedicine networks, such that local caregivers of the local caregiver networks 374 may provide care to patients or users via telemedical communications.
  • one or more of the third- party networks 368 may receive data and analysis from the Al wearable device network 348, for various purposes including but not limited to diagnosis, instruction, pandemic monitoring, disaster response, resource allocation, medical triage, contextual analysis, sensor data reconstruction, any other tracking or intervention and associated logistics, etc.
  • the first responder networks 370 may include any person or team with specialized training who is among the first to arrive and provide assistance at the scene of an emergency, such as an accident, natural disaster, terrorism, etc.
  • First responders include, but are not limited to, paramedics, emergency medical technicians (EMTs), police officers, fire fighters, etc.
  • the essential workforce networks 372 may include networks for employers and employees of essential workforces of any company or government organization that continues operation during times of crises, such as a viral pandemic.
  • Essential workforces include, but are not limited to, police, medical staff, grocery workers, pharmacy workers, other health and safety service workers, etc.
  • the local caregiver networks 374 may include a network of local clinics, family doctors, pediatricians, in-home nurses, nursing home staff, and other local caregivers.
  • the hospital networks 376 allow transfer of data between hospitals and the Al wearable device network 348.
  • the exchange of information between the Al wearable device network 348 and third- party networks 368 may involve use of a verification entity 386, which ensures data security in accordance with applicable rules and regulations (e.g., HIPAA).
  • the Al wearable device network 348 utilizes the third-party API module 350 to perform such verification of the third- party networks 368 utilizing the verification entity 386, before providing any data or analysis thereof related to the user 336 or crowd of users 338 to any of the third-party networks 368.
  • any data or analysis related to the user 336 or crowd of users 338 may be anonymized prior to being sent to one or more of the third-party networks 368, such as in accordance with privacy settings in user profiles (e.g., user profile 344 associated with the user 336, user profiles associated with respective users in the crowd of users 338, etc.).
  • the pandemic response module 352 is configured to execute processes based on receiving pandemic data from one or more of the third-party networks 368 via the third-party API module 350. The pandemic response module 352 may analyze such received information and provide notifications to the user 336 or crowd of users 338 including relevant information about the pandemic.
  • the pandemic response module 352 may further collect and analyze physiological data of the user 336 or crowd of users 338 that may be relevant to the pandemic, and provides instructions to users who may be at risk due to the pandemic. Information about such at-risk users may also be provided to one or more of the third-party networks 368. The pandemic response module 352 may continually update the database 366 with relevant pandemic data including information about at-risk users.
  • the pandemic response module 352, while described herein as processing information related to pandemics, may also be configured to process information related to epidemics and other outbreaks of diseases that do not necessarily reach the level of a pandemic.
  • the pandemic response module 352 may also process information from the user 336 and crowd of users 338 so as to predict that a pandemic, epidemic or other disease outbreak is or is likely to occur. Thus, the functionality of the pandemic response module 352 is not limited solely to use in processing pandemic information.
  • the vital monitoring module 354 may monitor and analyze physiological data of the user 336 and crowd of users 338 to detect and mitigate pandemics, epidemics and other outbreaks or potential outbreaks of diseases. The physiological data may be analyzed to determine if there is evidence of a disease associated with a pandemic (e.g., shortness of breath associated with respiratory illness).
  • the vital monitoring module 354 may also be utilized for monitoring user 336 and the crowd of users 338 before, during and after detection of firearm discharges. Such information, for example, may be provided to various ones of the third-party networks 368, such as the first responder networks 370, the military or other security networks 383, etc.
  • the location tracking module 356 is configured to track the location of user 336 and the crowd of users 338, such as to determine whether any of such users enter or exit regions associated with a pandemic or other outbreak of a disease.
  • the location tracking module 356, in some embodiments, may alert users who have entered a geographic location or region associated with increased risk of exposure to an infectious disease (e.g., associated with an epidemic, pandemic or other outbreak), a geographic location or region associated with a natural disaster, a geographic location or region associated with exposure to radiation, toxins or other potentially harmful environmental conditions, etc.
  • various alerts, notifications and safety instructions are provided to the user 336 and crowd of users 338 based on their location.
  • the threshold for detection of symptoms associated with an infectious disease may be modified based on location of the user 336 and crowd of users 338. For example, the threshold for detecting a symptom (e.g., shortness of breath) may be lowered if the user 336 or crowd of users 338 are in high-risk locations for contracting an infectious disease.
  • the location tracking module 356 may also be configured to track the locations of the user 336 and the crowd of users 338 before, during and after detection of firearm discharges. The tracked location may be provided to various ones of the third-party networks 368, such as the first responder networks 370, the military or other security networks 383, etc.
  • the automated contact tracing module 358 is configured to use the tracked location of the user 336 and crowd of users 338 (e.g., from the location tracking module 356) so as to determine possible contacts between such users, and also to assess risk of infection on a peruser basis.
  • the automated contact tracing module 358 may also automate the delivery of notifications to the user 336 and crowd of users 338 based on potential exposure to other users or geographic regions (e.g., associated with a pandemic or other outbreak of a disease, associated with exposure to radiation, toxins or other potentially harmful environmental conditions, etc.).
  • the automated contact tracing module 358 may further provide information regarding contacts between the user 336 and crowd of users 338 to one or more of the third- party networks 368 (e.g., indicating compliance with risk mitigation strategies for pandemic response, exposure to radiation, toxins or other harmful environmental conditions, etc.).
  • the disease progression module 360 is configured to analyze physiologic data from the user 336 and crowd of users 338, and to determine whether such physiologic data is indicative of symptoms of a disease. As new physiologic data from the user 336 and crowd of users 338 is received, trends in such data may be used to identify the progression of a pandemic or other outbreak of a disease.
  • the disease progression module 360 may be configured to monitor the progression of specific infectious diseases, such as infectious diseases associated with epidemics, pandemics or other outbreaks, based on any combination of: user indication of a contracted disease; one or more of the third-party networks 368 indicating that users have contracted a disease; the vital monitoring module 354 detecting a user contracting a disease with probability over some designated threshold; etc.
  • the disease progression module 360 is further configured to compare disease progress for different ones of the users 336 and crowd of users 338 with typical disease progress to determine individual user health risk.
  • the in-home module 362 is configured to analyze location data from the user 336 and crowd of users 338, and to determine whether any of such users are in locations with stay-at- home or other types of quarantine, social distancing or other self-isolation orders or recommendations in effect. If so, the in-home module 362 may provide notifications or alerts to such users with instructions for complying with the stay-at-home, quarantine, social distancing or other self-isolation orders or recommendations, for mitigating an infectious disease, for preventing spread of the infectious disease, etc.
  • the in-home module 362 may be further configured to provide in-home monitoring of infected patients that are quarantined or self-isolated at home, providing warnings to such users that leave the home, instructions for mitigating the disease, etc.
  • the in-home module 362 may further provide in-home monitoring data to one or more of the third-party networks 368.
  • the essential workforce module 364 is configured to identify ones of the user 336 and crowd of users 338 that are considered part of an essential workforce or are otherwise considered essential personnel. Once identified, the essential workforce users’ physiologic data may be analyzed to determine risk profiles for such users, and the algorithms implemented by modules 350 through 362 may be modified accordingly. As one example, the functionality of the in-home module 362 may be modified such that alerts or notifications are not sent to essential workforce users when leaving areas associated with stay-at-home, quarantine, social distancing or other self-isolation orders (e.g., those users would not receive alerts or notifications when traveling to or from their associated essential workplaces). Various other examples are possible, as will be described elsewhere herein.
  • pandemic response module 352 can further leverage the contextual and/or environmental data obtained from the accessory devices 315 in performing their various functionality using the contextual analysis module 365 and/or using sensor data reconstructed using the sensor data reconstruction module 367.
  • FIG. 3F shows a detailed view of the compartmentalized network sink node 390, which includes a computation unit 391 implementing compartmentalized network shuttling module 393.
  • the compartmentalized network shuttling module 393 provides functionality similar to that of the compartmentalized network shuttling modules 339, 349 and 369.
  • the compartmentalized network sink node 390 also includes a data storage unit 395, a communications unit 397, and a power supply 399.
  • the computation unit 391 may comprise at least one processing device which, together with the data storage unit 396, provides computational and data storage capability for the compartmentalized network sink node 390.
  • the computation unit 39 provides an onboard computational system that can offload data (e.g., collected from the user 336 and the crowd of users 338 using wearable devices and/or accessory devices), store data locally on the data storage unit 395, compute metrics and/or generate monitoring and treatment reports from data collected from the user 336 and the crowd of users 338 (e.g., communicated via intra-compartment localized mobility network nodes and inter-compartment high mobility network nodes within the compartmentalized monitoring environment 301).
  • the computation unit 391, in some embodiments, is configured to run a gamut of algorithms, to offload a combination of physiologic data from monitored subjects (e.g., the user 336 and crowd of users 338), etc.
  • the computation unit 39 may monitor identified tasks performed by nurses, doctors, caregivers or other support staff (e.g., via one or more applications on connected smart devices) in order to document what was performed for each of the monitored subjects, and to make that available for downstream review, to store in patient or subject records, etc.
  • the communications unit 397 is configured to provide various communications functionality for the compartmentalized network sink node 390.
  • the communications unit 397 includes one or more Universal Serial Bus (USB) ports, WiFi, Bluetooth, NFC or other communication capability to offload data from wearable devices and or wireless gateways which are paired with the compartmentalized network sink node 390 to the data storage unit 395.
  • the communications unit 397 may also include radio systems for long-range communication with remote medical facilities or other systems or devices (e.g., the Al wearable device network 348 and/or the third-party networks 368 via network 384) to offload data from the data storage unit 395. Such radio systems for long-range communication may also be used for receiving data from the remote medical facilities or other systems or devices.
  • the communications unit 397 may further provide for connection to any desired communication network (e.g., network 384) for data transmission.
  • the power supply 399 is configured to provide power to the computation unit 391, the data storage unit 395 and the communications unit 397.
  • the power supply 399 may also be configured to charge other devices (e.g., wearable devices such as wearable device 302, wireless gateways such as wireless gateway 340, smart devices such as one or more smartphones, etc.).
  • FIG. 4 shows a system 400 including a compartmentalized monitoring environment 401 including a first compartment 410-1 and a second compartment 410-2 which are connected via inter-compartment connection 415.
  • the first compartment 410-1 and the second compartment 410-2 include a plurality of network nodes, including intra-compartment localized mobility network nodes, inter-compartment high mobility network nodes, stationary network nodes, and at least one compartmentalized network sink node.
  • the intra-compartment localized mobility network nodes are able to travel or move within one of the first compartment 410-1 and the second compartment 410-2, while the inter-compartment high mobility network nodes are able to travel or move between the first compartment 410-1 and the second compartment 410-2 via the inter-compartment connection 415.
  • the stationary network nodes are assumed to be fixed and stay in place at particular locations in the first compartment 410-1 and the second compartment 410-2.
  • the at least one compartmentalized network sink node provides a data link between the compartmentalized monitoring environment 401 and an external network 420 (e.g., Al wearable device network 348, the third-party networks 368, etc.).
  • the compartmentalized monitoring environment 401 is shown as including just two compartments 410-1 and 410-2 with a single inter-compartment connection 415 therebetween. It should be appreciated, however, that the compartmentalized monitoring environment 401 may include more than two compartments, and that there may be more than one inter-compartment connection between two compartments. Further, an intercompartment connection may connect more than two compartments (e.g., a hallway connecting multiple rooms of a facility, with each of the rooms representing a compartment).
  • the compartments 410-1 and 410-2 of the compartmentalized monitoring environment 401 are assumed to represent different geographic or other locations which are at least partially isolated form one another.
  • the compartmentalized monitoring environment 401 may comprise a facility, where the first compartment 410-1 and the second compartment 410-2 are different rooms, floors or other sections of the facility.
  • the first compartment 410-1 and the second compartment 410-2 may comprise respective metal enclosed rooms within a facility.
  • the inter-compartment connection 415 may comprise a door, hatch, tunnel, stairway or other connection between the first compartment 410-1 and the second compartment 410-2.
  • the inter-compartment connection 415 is only open or accessible for limited times (e.g., when a door, hatch, tunnel, stairway, etc. is unlocked or opened). This may be the case, for example, when the facility in which the compartments 410- 1 and 410-2 are located is a secured facility (e.g., a military, medical or other facility where sensitive data may be handled within different ones of the compartments 410-1 and 410-2), a remote or austere environment such as a system of caves, mountains or other difficult to traverse terrain which limits mobility between geographic regions providing the compartments 410-1 and 410-2, etc.
  • a secured facility e.g., a military, medical or other facility where sensitive data may be handled within different ones of the compartments 410-1 and 410-2
  • a remote or austere environment such as a system of caves, mountains or other difficult to traverse terrain which limits mobility between geographic regions providing the compartments 410-1 and 410-2, etc.
  • the inter-compartment connection 415 is always open or accessible. This may be the case, for example, in a hospital where the compartments 410-1 and 410-2 are different rooms, floors, wings or other section of the hospital which generally are open to one another, but which may be isolated from one another for some time frame of interest where at least some network nodes are unlikely to move between the compartments 410-1 and 410-2 during the time frame of interest (e.g., patients in different hospital rooms with no or limited mobility, nurses, doctors or other staff of the hospital working in shifts where such staff generally stays within one area for the duration of a shift or between breaks, etc.).
  • the compartments 410-1 and 410-2 are different rooms, floors, wings or other section of the hospital which generally are open to one another, but which may be isolated from one another for some time frame of interest where at least some network nodes are unlikely to move between the compartments 410-1 and 410-2 during the time frame of interest (e.g., patients in different hospital rooms with no or limited mobility, nurses, doctors or other staff of the
  • data transfer is managed in the compartmentalized monitoring environment 401 to allow mobile network nodes in the system 400 (e.g., the intra-compartment localized mobility network nodes and the inter-compartment high mobility network nodes) which are connected with physical objects (e.g., humans, robots, etc.) that move within the compartmentalized monitoring environment 401 to assist with data transfer across dead zones.
  • the intra-compartment localized mobility network nodes and the intercompartment high mobility network nodes comprise gateway devices (e.g., wireless gateway 340) which provide a way to have both fixed and mobile network nodes in a network to support a mesh with full duplex communications.
  • the mobile network nodes may be worn be humans, who are generally able to move at least one of within one of the first compartment 410-1 and the second compartment 410-2 and between the first compartment 410-1 and the second compartment 410-2.
  • the mobile network nodes e.g., the intra-compartment localized mobility network nodes and the inter-compartment high mobility network nodes
  • data package pickup and delivery by network nodes in the compartmentalized monitoring environment 401 may be registered (e.g., a next time two network nodes in the compartmentalized monitoring environment 401 communicate with each other, they can acknowledge delivery of any relevant data packages).
  • Each of the network nodes within the compartmentalized monitoring environment 401 may be given a mobility ranking or score. The mobility ranking or score associated with a given network node may depend on how the given network node interacts with other network nodes in the compartmentalized monitoring environment 401.
  • each network node in the compartmentalized monitoring environment 401 establishes a mobility rating over time, with data package delivery being prioritized from lower mobility network nodes to higher mobility network nodes.
  • a mesh network in the compartmentalized monitoring environment 401 organizes into one that can manage packet delivery priorities.
  • Mobility rankings are established based on how many network nodes in the compartmentalized monitoring environment 401 over time that a particular network node encounters.
  • this enables data from all network nodes in the mesh to reach compartmentalized network sink nodes even though some network nodes may never come into range of one of the compartmentalized network sink nodes, and there may never be a real-time connection between some network nodes and one of the compartmentalized network sink nodes.
  • the “role” of any given network node in the compartmentalized monitoring environment 401 may change over time.
  • non-fixed network nodes may change roles (e.g., between the stationary network node role, the intra-compartment localized mobility network node role and the intra-compartment high mobility network node role) based on their mobility rankings, which may be dynamic and are updated over time.
  • a given network node e.g., a smartphone
  • the compartmentalized monitoring environment 401 may have various lockers or other storage components in one or both of the compartments 410-1 and 410-2.
  • the given network node may be determined to have the stationary network node role for the given time period of interest. If the given user attaches the given network node to a charging station within one of the compartments 410-1 and 410-2 for a given time period of interest (e.g., while the given network node charges to at least a designated charge level), then the given network node may be determined to have the stationary network node role for the given time period of interest.
  • the given network node may be determined to have either the intra-compartment localized mobility network node role of the inter-compartment high mobility network node role depending on its dynamic mobility ranking.
  • the given network node may be determined to have the intra-compartment localized mobility network node role for the given time period of interest.
  • the given user may be one which works in multiple areas of a facility (e.g., both the first compartment 410-1 and the second compartment 410-2 of the compartmentalized monitoring environment 401) for a given time period of interest (e.g., during a work shift, between breaks of the work shift, etc.).
  • the given network node may be determined to have the intercompartment high mobility network node role for the given time period of interest.
  • a given user may have different network node roles at different times during a work shift or between breaks of a work shift.
  • a nurse, doctor or other staff may for a first period of time perform “rounds” through a facility (e.g., such that network nodes associated with such users have the inter-compartment high mobility network node role for the first period of time) and for a second period of time may work in an office, may be performing a surgical or other procedure in one location, etc. (e.g., such that network nodes associated with such users have the intra-compartment localized mobility network node role or the stationary network node role for the second period of time).
  • a facility e.g., such that network nodes associated with such users have the inter-compartment high mobility network node role for the first period of time
  • a second period of time may work in an office, may be performing a surgical or other procedure in one location, etc.
  • network nodes associated with such users have the intra-compartment localized mobility network node role or
  • network nodes may take on the compartmentalized network sink node role at different times, such as depending on when network nodes are able to establish a data link with the external network 420 (e.g., when a gateway device, a smart device such as a smartphone, etc. does or does not have a cellular network connection to the external network 420).
  • data may be temporarily sent to and held by different ones of the network nodes.
  • the more mobile ones of the network nodes hold such data (e.g., shuttled data packets or packages) and hand off the data to other network nodes which have the compartmentalized network sink node role, or which are closer to or more likely to move closer to ones of the network nodes having the compartmentalized network sink node role.
  • the second network node will eventually hand that data off to a compartmentalized network sink node or another network node that is closer to or more likely to move closer to a compartmentalized network sink node.
  • the second network node may give a transmission receipt to the first network node (e.g., indicating that its data has been handed off to a compartmentalized network sink node or another network node that is closer to or more likely to move closer to a compartmentalized network sink node).
  • This transmission receipt may prompt the first network node to send or hand over more data (e.g., destined for the external network 420) to the second network node.
  • data may be “shuttled” (e.g., quickly or slowly) in the compartmentalized monitoring environment 401 towards the compartmentalized network sink nodes, even though not direct wireless communication is happening between some of the network nodes and the compartmentalized network sink nodes.
  • data may also be shuttled from the external network 420 to different network nodes in the compartmentalized monitoring environment 401.
  • the compartmentalized monitoring environment 401 includes one or more stationary network nodes and/or one or more powered network nodes.
  • the stationary network nodes may accept data (e.g., data packets or packages) from one or more network nodes and hold such data for future release to one or more other network nodes. This may include, for example, receiving data from intra-compartment localized mobility network nodes and holding it for release to inter-compartment high mobility network nodes (e.g., which are able to reach compartmentalized network sink nodes and the external network 420), receiving data from inter-compartment high mobility network nodes and holding it for related to intra-compartment localized mobility network nodes (e.g., for relaying data from the external network 420 to such intra-compartment localized mobility network nodes or stationary network nodes reachable via the intra-compartment localized mobility network nodes, etc.).
  • inter-compartment high mobility network nodes e.g., which are able to reach compartmentalized network sink nodes and the external network 420
  • the stationary network nodes may be configured to send data from one stationary network node to another very quickly when an opportunity arises. This fast delivery may happen, for example, between a first stationary network node in the first compartment 410-1 and a second stationary network node 410-2 in the second compartment 410-2 when the inter-compartment connection 415 between the first compartment 410-1 and the second compartment 410-2 opens (e.g., when a hatch, stairway, hallway, door or other type of inter-compartment connection 415 is at least temporarily open), such as when a subject (e.g., associated with one of the inter-compartment high mobility network nodes) is traveling through the inter-compartment connection 415 (e.g., climbing or walking through a hatch, stairway, hallway, door or other type of intercompartment connection 415).
  • a subject e.g., associated with one of the inter-compartment high mobility network nodes
  • each node in the network may be assigned a mobility score, the mobility score related to the number of nodes within the network that the mobile node communicates with over a period of time, optionally combined with a priority score related to how often the mobile node communicates with a network sink, or a hop distance to the network sink.
  • the network may reorganize data traffic and data temporary storage conditions on the mobility scores of the various nodes.
  • the storage algorithm may be designed to maximize data transfer rates to exits depending on the potentially changing movements of the mobile nodes within the network.
  • the network may run a machine learned data transfer optimization algorithm based on early data collection effectiveness, and updated node scoring and data transfer metrics may be updated to the network periodically to reoptimize dataflow based upon the recent behavior of nodes in the network.
  • the network nodes in the compartmentalized monitoring environment 401 include or are connected with local sensors such as physiologic monitors, chemical sensors, environmental detectors, smoke detectors, Internet of Things (loT) devices, etc.
  • a given network node may comprise a sensing device (e.g., sensing device 210, accessory device 215, stimulating device 220, wearable device 302, accessory sensing device 315, etc.), a gateway device (e.g., wireless gateway 340, a PCD), a host device (e.g., host device 145, host device 230, a carrying case of a rapid deployment physiologic monitoring kit, a deployable device paired with a carrying case of a rapid deployment physiologic monitoring kit, a smart device such as a smartphone, tablet, etc.), combinations thereof, etc.
  • a sensing device e.g., sensing device 210, accessory device 215, stimulating device 220, wearable device 302, accessory sensing device 315, etc.
  • a gateway device e.g., wireless
  • the external network 420 may comprise a computing center, where the computing center may perform fusion of sensor data from multiple ones of the network nodes in the compartmentalized monitoring environment 401. It should be noted, however, that in some cases fusion of sensor data may be performed locally within the compartmentalized monitoring environment 401 utilizing one or more of the network nodes located therein.
  • data may be stored at any of the network nodes for a period of time before being moved forward (e.g., shuttled) to other network nodes.
  • Messages or other data may be sent to and received from the external network 420 via the compartmentalized network sink nodes.
  • Messages or other data may be distributed to network nodes within the compartmentalized monitoring environment 401 (e.g., from a compute hub or node either within the compartmentalized monitoring environment 401 or in or accessible via the external network 420).
  • the mesh network of the compartmentalized monitoring environment 401 can be established by the network nodes (e.g., gateway devices such as wireless gateway 340) therein automatically, can be self-healing, and does not need to rely on any network infrastructure from the environment.
  • the network nodes e.g., gateway devices such as wireless gateway 340
  • at least a subset of the network nodes in the compartmentalized monitoring environment 401 are gateway devices provided in a plug-over- plug form to charge and maintain all system components without additional hardware.
  • ones of the network nodes which are stationary network nodes may be embodied as stationary gateway devices or standalone access points including means for charging other ones of the network nodes (e.g., sensors, gateways, system components, etc.).
  • the stationary network nodes may also act as storage and relay network nodes in the compartmentalized monitoring environment 401.
  • the stationary network nodes may include one or more sensors for assessing the environment.
  • the stationary network nodes act as “anchors” for establishing micro-location and tracking movement patterns of other network nodes in the mesh network of the compartmentalized monitoring environment.
  • the stationary network nodes in the first compartment 410-1 and the second compartment 410-2 can use that moment (e.g., when the inter-compartment connection 415 is open) to quickly shuttle data between the first compartment 410-1 and the second compartment 410-2.
  • the process 500 includes steps 502 through 504.
  • the process 500 may be performed, for example by a given one of a plurality of network nodes in a compartmentalized monitoring environment comprising two or more compartments and at least one inter-compartment connection interconnecting a first one of the two or more compartments and a second one of the two or more compartments.
  • the given network node may comprise a smart device associated with a given user (e.g., a smartphone, a tablet computing device, etc.), a wearable device associated with a given user, a gateway device associated with a given user, a standalone wireless access point, an edge computing device, a sensor device (e.g., an loT sensor device), a carrying case of a rapid deployment physiologic monitoring kit, etc.
  • the at least one inter-compartment connection may only intermittently connect the first compartment and the second compartment.
  • the compartmentalized monitoring environment may comprise a facility, the two or more compartments comprise at least one of rooms and floors of the facility, and the at least one inter-compartment connection comprises at least one of a door, a hatch, a hallway, and a stairway in the facility.
  • the two or more compartments may comprise two or more geographic regions of a remote monitoring environment, at least one of the two or more geographic regions having no or limited connectivity to an external network outside the remote monitoring environment.
  • the given network node joins a mesh network for the compartmentalized monitoring environment, the mesh network comprising at least a subset of the plurality of network nodes.
  • the mesh network may be formed on one or more physical layers, each of the one or more physical layers comprising a range of frequency bands upon which data can be transferred, and wherein the mesh network utilizes at least one mesh protocol based on at least one of a long range (LoRa) evolution schema, an ultrawideband (UWB) mesh protocol, a Bluetooth Low Energy (BLE) mesh protocol, a WiFi mesh protocol, a HaLow mesh protocol, and a private 5G mesh protocol.
  • LiRa long range
  • UWB ultrawideband
  • BLE Bluetooth Low Energy
  • the given network node communicates data between the two or more compartments of the compartmentalized monitoring environment by at least one of obtaining at least a portion of the data from and providing at least a portion of the data to at least one other network node of the plurality of network nodes that is within range of the given network node in the mesh network.
  • Each network node in the plurality of network nodes may be associated with at least one of a set of two or more network node roles for at least a portion of a designated period of time.
  • the given network node is associated with a first one of the set of two or more network node roles for a first portion of the designated period of time and is associated with a second one of the two or more network node roles for a second portion of the designated period of time.
  • the set of two or more network node roles may comprise: an intra-compartment mobile network node role associated with ones of the plurality of network nodes having mobility limited to a single one of the two or more compartments of the compartmentalized monitoring environment for at least a portion of the designated period of time; an intercompartment mobile network node role associated with ones of the plurality of network nodes having mobility across at least two of the two or more compartments of the compartmentalized monitoring environment for at least a portion of the designated period of time; a stationary network node role associated with ones of the plurality of network nodes in a fixed position within one of the two or more compartments of the compartmentalized monitoring environment for at least a portion of the designated period of time; and a compartmentalized network sink node role associated with ones of the plurality of network nodes capable of establishing a data link, distinct from the mesh network, with at least one external network outside the compartmentalized monitoring environment for at least a portion of the designated period of time.
  • Network nodes associated with the stationary network node role may provide anchor points for establishing location and movement patterns of one or more other ones of the plurality of network nodes in the compartmentalized monitoring environment.
  • Network nodes associated with the stationary network node role may be configured to charge at least one other one of the plurality of network nodes.
  • a network node may be associated with both the compartmentalized network sink node role and one of the intra-compartment mobile network node role and the inter-compartment mobile network node role for at least a portion of the designated period of time.
  • At least a first one of the plurality of network nodes may be configured to obtain data from a second one of the plurality of network nodes which is destined for a third one of the plurality of network nodes, the first network node being configured to store the obtained data until the first network node hands off the obtained data to the third network node or a fourth network node that is predicted to be within range of the third network node before the first network node is predicted to be within range of the third network node.
  • the third network node may comprise a compartmentalized network sink node capable of establishing a data link, distinct from the mesh network, with at least one external network outside the compartmentalized monitoring environment.
  • the first network node may be configured to provide a transmission receipt to the second network node.
  • the third network node may be configured to perform analysis utilizing the obtained data from the second network node and additional obtained data from one or more additional ones of the plurality of network nodes.
  • the analysis may comprise a fusion of sensor data from a plurality of sensors associated with at least a subset of the plurality of network nodes including the second network node and the one or more additional ones of the plurality of network nodes.
  • Each of the plurality of network nodes in the compartmentalized monitoring environment may be assigned a mobility ranking score, the mobility ranking score for the given network node being assigned based at least in part on how the given network node interacts with other ones of the plurality of network nodes in the compartmentalized monitoring environment.
  • the mobility ranking score for the given network node may be determined based at least in part on how many other ones of the plurality of network nodes come within range of the given network node over a designated period of time.
  • the mobility ranking score for the given network node may also or alternatively be determined based at least in part on a number of network hops between the given network node and a closest other one of the plurality of network nodes which comprises a compartmentalized network sink node capable of establishing a data link, distinct from the mesh network, with at least one external network outside the compartmentalized monitoring environment.
  • the data may be communicated in step 204 using ones of the plurality of network nodes selected based at least in part on their assigned mobility ranking scores.
  • the mobility ranking scores assigned to the plurality of network nodes may be dynamically updated over time.

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Abstract

Un système comprend un environnement de surveillance compartimenté comprenant deux compartiments ou plus, au moins une connexion inter-compartiment interconnectant un premier compartiment des deux compartiments ou plus et un second compartiment des deux compartiments ou plus, ainsi qu'une pluralité de nœuds de réseau. La pluralité de nœuds de réseau font partie d'un réseau maillé pour l'environnement de surveillance compartimenté, et la pluralité de nœuds de réseau sont configurés pour communiquer des données entre les deux compartiments ou plus de l'environnement de surveillance compartimenté.
PCT/US2024/042294 2023-08-29 2024-08-14 Communication de données par l'intermédiaire de nœuds de réseau mobile dans des environnements de surveillance compartimentés Pending WO2025049109A2 (fr)

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US20050190778A1 (en) * 2004-02-27 2005-09-01 Interdigital Technology Corporation Multi-system mesh network
US20150071163A1 (en) * 2013-09-12 2015-03-12 Olea Networks, Inc. Portable Wireless Mesh Device
EP3141010B1 (fr) * 2014-06-24 2019-09-11 Google LLC Inbetriebnahme eines mesh-netzwerks
US9634928B2 (en) * 2014-09-29 2017-04-25 Juniper Networks, Inc. Mesh network of simple nodes with centralized control
EP4197483B1 (fr) * 2017-07-28 2026-01-28 LifeLens Technologies, Inc. Kits de surveillance physiologique

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