WO2018120096A1 - System and method for algorithm adjustment applying motion sensors in cgm system - Google Patents

System and method for algorithm adjustment applying motion sensors in cgm system Download PDF

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Publication number
WO2018120096A1
WO2018120096A1 PCT/CN2016/113676 CN2016113676W WO2018120096A1 WO 2018120096 A1 WO2018120096 A1 WO 2018120096A1 CN 2016113676 W CN2016113676 W CN 2016113676W WO 2018120096 A1 WO2018120096 A1 WO 2018120096A1
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patient
processer
algorithms
motion sensor
cgm
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PCT/CN2016/113676
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French (fr)
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Cuijun YANG
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Medtrum Technologies Inc
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Medtrum Technologies Inc
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Priority to PCT/CN2016/113676 priority Critical patent/WO2018120096A1/en
Priority to EP16924946.3A priority patent/EP3562380A4/en
Priority to US16/470,559 priority patent/US11412994B2/en
Publication of WO2018120096A1 publication Critical patent/WO2018120096A1/en
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient; User input means
    • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
    • A61B5/0022—Monitoring a patient using a global network, e.g. telephone networks, internet
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/1118—Determining activity level
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/14503—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/1495—Calibrating or testing of in-vivo probes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7225—Details of analogue processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient; User input means
    • A61B5/7405—Details of notification to user or communication with user or patient; User input means using sound
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/02—Operational features
    • A61B2560/0223—Operational features of calibration, e.g. protocols for calibrating sensors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters

Definitions

  • This invention generally relates to medical appliance, and more specifically to a system and method for algorithm adjustment applying motion sensors in a CGM system.
  • the pancreas produces and releases insulin into the blood stream in response to elevated blood glucose levels.
  • ⁇ -cells which reside in the pancreas, produce and secrete the insulin into the blood stream, as it is needed. If ⁇ -cells become incapacitated or die, a condition known as Type I diabetes mellitus, or in some cases if ⁇ -cells produce insufficient quantities of insulin, Type II diabetes, then insulin must be provided to the body of the patient from another source.
  • infusion pump therapy has been increasing, especially for delivering insulin for diabetics.
  • external infusion pumps are worn on a belt, in a pocket, or patched to the body of the patient directly, and deliver insulin into the body by an infusion tube with a percutaneous needle or a cannula placed in the subcutaneous tissue.
  • the medicine that is delivered by the infusion pump device can depend on the condition of the patient and the desired treatment plan.
  • current insulin pumps and other diabetes treatment instruments are limited in switching between different treatment plans based on different conditions of the patient.
  • Desired treatment plans depend on accurate determination of different conditions of the patient, especially for the continuously glucose monitoring in tissue fluid whose concentration is easily influenced by the actions of the patient. If a patient is in sleep, due to less activity taking place in the muscle and organs than the normal state, whether she or he being in a state of low blood glucose needs to be recalculated by an adjusted algorithm. Furthermore, some low-priority alerts should be muted to prevent disturbing the patient from sleep. Similarly, if a patient is doing physical exercise, her or his blood glucose value may fluctuate sharply, but her or his blood glucose level should not be determined as abnormal, and this false “abnormal fluctuations” of the blood glucoses level should be excluded. In pursuing desirable treatment plans, the combination of sensing the activity level of the patient and adjusting the blood glucose related algorithms to provide more accurate data became crucial.
  • one purpose of the present invention is to provide a method for adjusting blood glucose related algorithms in a continuous glucose monitoring (CGM) system, comprising,
  • the motion sensor comprises one or more from an accelerometer, a gyroscope and an attitude sensor.
  • the method further comprises adjusting the algorithms according to different exercise intensities when the patient is in a state of physical exercise.
  • the blood glucose related algorithms comprise but not limited to a filtering algorithm configured to calculate the blood glucose value, a predictive low glucose algorithm and an alert threshold algorithm.
  • the method further comprises automatically switch the CGM system into an audio-off mode for low-priority alerts that do not require immediate action according to the adjusted algorithm.
  • the method further comprises excluding abnormal fluctuations of the blood glucose sensor data by adjusting related algorithms via the processer when the patient is determined in a state of physical exercise via the processer.
  • the other purpose of the present invention is to provide a system using the method for adjusting blood glucose related algorithms identified above, comprising a CGM system with a processer and at least one motion sensor set in the CGM system.
  • the motion sensor is configured to sense the activity levels of a patient and provide corresponding signals; and the processer is configured to determine the physiological states and exercise intensities of the patient and adjust blood glucose related algorithms depending partly on the signals from the motion sensor.
  • the glucose data processed by the processer using the adjusted algorithm might be sent to a handset or a smart device to display or be further processed to control a patch pump.
  • the present invention has advantages in the following ways: Firstly, applying the motion sensor in the CGM system enables a comprehensive grasp of the patient's activity levels for a more rational treatment by distinguishing sleep and physical exercise states from the normal state, adjusting blood glucose related algorithms according to different activity levels and exercise intensities of the patient provides more reliable data that leads directly to appropriate treatments. Secondly, because the continuously glucose monitoring system detects the glucose level in tissue fluid which is easily influenced by the attitude and activity level of a subject, excluding abnormal fluctuation of the sensor glucose level better reflects the real situation of the patient. Thirdly, muting some low-priority alerts when the patient is determined in the state of sleep or exercise reduces unnecessary disturbance to the patient, making the system more pleasant to use.
  • the application of motion sensor in the CGM system enables algorithm adjustments based on different physical states and exercise intensities of the patient to provide more accurate and reliable blood glucose related data that is the basis of desirable treatment plans, and a CGM system using this method satisfies the requirements of the patient on safety and intelligence of a glucose monitoring device in a more sophisticated way.
  • FIG. 1 is a schematic diagram of a patient wearing a CGM system in the present invention
  • FIG. 2 is a schematic diagram of the CGM system in the present invention
  • FIG. 3 is a schematic diagram of the representative method in an embodiment of the present invention.
  • FIG. 4 is a flow chart of the representative method in an embodiment of the present invention.
  • FIG. 5 is a flow chart of the representative method in an embodiment of the present invention.
  • FIG. 1 illustrates a patient wearing a CGM system 1 configured to monitor the blood glucose changes of the patient in real time.
  • FIG. 2 illustrates the structure of the CGM system 1, comprising a motion sensor 101 and a processer 102.
  • a motion sensor 101 is set in the CGM system 1, configured to sense activity levels of the patient and send corresponding signals to the processer 102.
  • the motion sensor 101 is a three-axis accelerometer 101, sensing the activity levels and state changes of the patient in three axes, and the processer 102 receives signals from the three-axis accelerometer 101 and adjusts corresponding algorithms depending partly on the signals.
  • ACC power is the acceleration amplitude of all three axes
  • ACC X is the acceleration data of the X axis
  • ACC Y is the acceleration data of the Y axis
  • ACC Z is the acceleration data of the Z axis.
  • the attitudes of the patient can be sensed by the three-axis accelerometer 101.
  • the attitude changes of the patient can be tracked by the three-axis accelerometer 101 in real time.
  • the state can be determined by the equation:
  • ACC var (ACC X -ACC X
  • ACC var is the acceleration variation of all three axes
  • ACC X is the acceleration data of the X axis
  • ACC Y is the acceleration data of the Y axis
  • ACC Z is the acceleration data of the Z axis
  • PRE is the acceleration data of the X axis at a previous time
  • PRE is the acceleration data of the Y axis at a previous time
  • PRE is the acceleration data of the Z axis at a previous time.
  • a motion sensor 101 is set in the CGM system 1 to sense activity levels of the patient and send corresponding signals.
  • a processer 102 set in the CGM system 1 receives signals from the motion sensor 101 and adjusts related algorithms depending partly on the signals, and the data processed using the adjusted algorithms is sent to a handset 31 or a smart phone 32 to display.
  • FIG. 4 is a flow chart of an exemplary method illustrating the co-operation of the accelerometer and the processer set in the CGM system.
  • an accelerometer or an attitude sensor in this embodiment, an accelerometer set in the CGM system.
  • the accelerometer senses an activity level of the patient.
  • the accelerometer provides signals indicative of the activity level to a processer in the CGM system.
  • the processer in the CGM system determines whether the patient is in a sleep or physical exercise state according to the signals from the accelerometer.
  • the processer adjusts a series of related algorithms depending partly on the signals from the accelerometer, including but not limited to a filtering algorithm to calculate the blood glucose value as illustrated at block 131, a predictive low glucose algorithm as illustrated at block 132, and an alert threshold algorithm as illustrated at block 133.
  • a filtering algorithm to calculate the blood glucose value as illustrated at block 131
  • a predictive low glucose algorithm as illustrated at block 132
  • an alert threshold algorithm as illustrated at block 133.
  • the processer will automatically switch the CGM system into an audio-off mode as illustrated at block 135, avoiding disturbing the patient in her or his normal sleep or normal exercise.
  • FIG. 5 is a flow chart of an exemplary method illustrating the co-operation of the motion sensor and the processer when the patient is in the state of physical exercise.
  • concentration of her or his tissue fluid may go through instant dramatic changes because of squeezing and stretching actions, so her or his glucose level sensed by a glucose sensor may fluctuate sharply but should not be determined as abnormal.
  • an accelerometer senses a sharp fluctuation in activity level of the patient.
  • the accelerometer provides signals indicative of the activity level to the processer in the CGM system.
  • the processer in the CGM system determines the patient is in a physical exercise state according to the signals from the accelerometer.
  • the processer adjusts a series of algorithms depending partly on the signals from the accelerometer.
  • the processer forbids a calibration of the glucose sensor for the reason that the calibration result would be unreliable during a fast-changing period of the glucose level.
  • the processer excludes the abnormal fluctuation using the adjusted algorithm.
  • the processer allows the calibration of the glucose sensor when the abnormal fluctuation is excluded.

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Abstract

A method for algorithm adjustment applying a motion sensor in a Continuous Glucose Monitoring system(1) and a system using the method, comprising sensing an activity level of a patient by a motion sensor(101) and providing signals to a processer(102), then adjusting a series of related algorithms depending partly on the signals by the processer(102) to provide more accurate and reliable blood glucose related data that is the basis of desirable treatment plans; and automatically operating the CGM system(1) including switching the system to an audio-off mode or pausing a calibration of the glucose sensor by the processer(102).

Description

[Title established by the ISA under Rule 37.2] SYSTEM AND METHOD FOR ALGORITHM ADJUSTMENT APPLYING MOTION SENSORS IN CGM SYSTEM TECHNICAL FIELD
This invention generally relates to medical appliance, and more specifically to a system and method for algorithm adjustment applying motion sensors in a CGM system.
BACKGROUND
For a normal healthy person, the pancreas produces and releases insulin into the blood stream in response to elevated blood glucose levels. β-cells, which reside in the pancreas, produce and secrete the insulin into the blood stream, as it is needed. If β-cells become incapacitated or die, a condition known as Type I diabetes mellitus, or in some cases if β-cells produce insufficient quantities of insulin, Type II diabetes, then insulin must be provided to the body of the patient from another source.
Traditionally, since insulin cannot be taken orally, insulin has been injected with a syringe. More recently, use of infusion pump therapy has been increasing, especially for delivering insulin for diabetics. For example, external infusion pumps are worn on a belt, in a pocket, or patched to the body of the patient directly, and deliver insulin into the body by an infusion tube with a percutaneous needle or a cannula placed in the subcutaneous tissue. The medicine that is delivered by the infusion pump device can depend on the condition of the patient and the desired treatment plan. However, current insulin pumps and other diabetes treatment instruments are limited in switching between different treatment plans based on different conditions of the patient.
Desired treatment plans depend on accurate determination of different conditions of the patient, especially for the continuously glucose monitoring in tissue fluid whose concentration is easily influenced by the actions of the patient. If a patient is in sleep, due to less activity taking place in the muscle and organs than the normal state, whether she or he being in a state of low blood glucose needs to be recalculated by an adjusted algorithm. Furthermore, some low-priority alerts should be muted to prevent disturbing the patient from sleep. Similarly, if a patient is doing physical exercise, her or his blood glucose value may fluctuate sharply, but her or his blood glucose level should not be determined as abnormal, and this false “abnormal fluctuations” of the blood glucoses level should be excluded. In pursuing desirable treatment plans, the combination of sensing the activity level of the patient and adjusting the blood glucose related algorithms to provide more accurate data became crucial.
SUMMARY OF THE INVENTION
To overcome the deficiencies of the prior art, one purpose of the present invention is to  provide a method for adjusting blood glucose related algorithms in a continuous glucose monitoring (CGM) system, comprising,
sensing an activity level of a patient by at least one motion sensor and providing signals indicative of the activity level to a processer in the CGM system；
determining the physical state of the patient according to the activity level and adjusting a plurality of algorithms via the processer depending partly on the signals from the motion sensor when the patient is determined in a sleep or physical exercise state.
Alternatively, the motion sensor comprises one or more from an accelerometer, a gyroscope and an attitude sensor.
Alternatively, the method further comprises adjusting the algorithms according to different exercise intensities when the patient is in a state of physical exercise.
Alternatively, the blood glucose related algorithms comprise but not limited to a filtering algorithm configured to calculate the blood glucose value, a predictive low glucose algorithm and an alert threshold algorithm.
Alternatively, the method further comprises automatically switch the CGM system into an audio-off mode for low-priority alerts that do not require immediate action according to the adjusted algorithm.
Alternatively, the method further comprises excluding abnormal fluctuations of the blood glucose sensor data by adjusting related algorithms via the processer when the patient is determined in a state of physical exercise via the processer.
Alternatively, when a calibration of the blood glucose sensor is performed at the same time of an abnormal fluctuation taking place, related algorithm is adjusted via the processer to pause the calibration until the abnormal fluctuation is excluded.
The other purpose of the present invention is to provide a system using the method for adjusting blood glucose related algorithms identified above, comprising a CGM system with a processer and at least one motion sensor set in the CGM system.
The motion sensor is configured to sense the activity levels of a patient and provide corresponding signals； and the processer is configured to determine the physiological states and exercise intensities of the patient and adjust blood glucose related algorithms depending partly on the signals from the motion sensor.
The glucose data processed by the processer using the adjusted algorithm might be sent to a handset or a smart device to display or be further processed to control a patch pump.
The present invention has advantages in the following ways: Firstly, applying the motion sensor in the CGM system enables a comprehensive grasp of the patient's activity levels for a  more rational treatment by distinguishing sleep and physical exercise states from the normal state, adjusting blood glucose related algorithms according to different activity levels and exercise intensities of the patient provides more reliable data that leads directly to appropriate treatments. Secondly, because the continuously glucose monitoring system detects the glucose level in tissue fluid which is easily influenced by the attitude and activity level of a subject, excluding abnormal fluctuation of the sensor glucose level better reflects the real situation of the patient. Thirdly, muting some low-priority alerts when the patient is determined in the state of sleep or exercise reduces unnecessary disturbance to the patient, making the system more pleasant to use. To sum up, the application of motion sensor in the CGM system enables algorithm adjustments based on different physical states and exercise intensities of the patient to provide more accurate and reliable blood glucose related data that is the basis of desirable treatment plans, and a CGM system using this method satisfies the requirements of the patient on safety and intelligence of a glucose monitoring device in a more sophisticated way.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a schematic diagram of a patient wearing a CGM system in the present invention
FIG. 2 is a schematic diagram of the CGM system in the present invention
FIG. 3 is a schematic diagram of the representative method in an embodiment of the present invention
FIG. 4 is a flow chart of the representative method in an embodiment of the present invention
FIG. 5 is a flow chart of the representative method in an embodiment of the present invention
DETAILED DESCRIPTION
To make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, the embodiments of the present invention are described in the following through specific embodiments.
Referring to FIG. 1 and FIG. 2, an embodiment of the present invention is provided. FIG. 1 illustrates a patient wearing a CGM system 1 configured to monitor the blood glucose changes of the patient in real time. FIG. 2 illustrates the structure of the CGM system 1, comprising a motion sensor 101 and a processer 102.
As shown in FIG. 2, a motion sensor 101 is set in the CGM system 1, configured to sense activity levels of the patient and send corresponding signals to the processer 102. In this embodiment, the motion sensor 101 is a three-axis accelerometer 101, sensing the activity levels and state changes of the patient in three axes, and the processer 102 receives signals from the three-axis accelerometer 101 and adjusts corresponding algorithms depending partly on the signals.
When the patient is in physical exercise, the start and end of the exercise, as well as the intensity of the exercise can be determined by the equation:
Figure PCTCN2016113676-appb-000001
Where,
ACCpower is the acceleration amplitude of all three axes；
ACCX is the acceleration data of the X axis；
ACCY is the acceleration data of the Y axis；
ACCZ is the acceleration data of the Z axis.
The attitudes of the patient, whether she or he is standing, sitting, lying, or changing from one of these attitudes to another, can be sensed by the three-axis accelerometer 101. In other words, the attitude changes of the patient can be tracked by the three-axis accelerometer 101 in real time. When the patient goes to sleep, the state can be determined by the equation:
ACCvar＝(ACCX-ACCX|PRE)2+(ACCY-ACCY|PRE)2+(ACCZ-ACCZ|PRE)2
Where,
ACCvar is the acceleration variation of all three axes；
ACCX is the acceleration data of the X axis；
ACCY is the acceleration data of the Y axis；
ACCZ is the acceleration data of the Z axis；
ACCX|PRE is the acceleration data of the X axis at a previous time；
ACCY|PRE is the acceleration data of the Y axis at a previous time；
ACCZ|PRE is the acceleration data of the Z axis at a previous time.
Referring to FIG. 3, an embodiment of the present invention is provided. As shown in FIG. 3, a motion sensor 101 is set in the CGM system 1 to sense activity levels of the patient and send corresponding signals. A processer 102 set in the CGM system 1 receives signals from the motion sensor 101 and adjusts related algorithms depending partly on the signals, and the data processed using the adjusted algorithms is sent to a handset 31 or a smart phone 32 to display.
FIG. 4 is a flow chart of an exemplary method illustrating the co-operation of the accelerometer and the processer set in the CGM system. When a patient goes to sleep or physical exercise, her or his change of state can be sensed by an accelerometer or an attitude sensor, in this embodiment, an accelerometer set in the CGM system. At block 10, the accelerometer senses an activity level of the patient. At block 11, the accelerometer provides signals indicative of the activity level to a processer in the CGM system. At block 12, the processer in the CGM system determines whether the patient is in a sleep or physical exercise  state according to the signals from the accelerometer. At block 13, the processer adjusts a series of related algorithms depending partly on the signals from the accelerometer, including but not limited to a filtering algorithm to calculate the blood glucose value as illustrated at block 131, a predictive low glucose algorithm as illustrated at block 132, and an alert threshold algorithm as illustrated at block 133. At block 134, when an alert is determined to be low priority according to the adjusted alert threshold calculating algorithm, the processer will automatically switch the CGM system into an audio-off mode as illustrated at block 135, avoiding disturbing the patient in her or his normal sleep or normal exercise.
FIG. 5 is a flow chart of an exemplary method illustrating the co-operation of the motion sensor and the processer when the patient is in the state of physical exercise. When the patient is doing physical exercise, the concentration of her or his tissue fluid may go through instant dramatic changes because of squeezing and stretching actions, so her or his glucose level sensed by a glucose sensor may fluctuate sharply but should not be determined as abnormal. At block 20, an accelerometer senses a sharp fluctuation in activity level of the patient. At block 21, the accelerometer provides signals indicative of the activity level to the processer in the CGM system. At block 22, the processer in the CGM system determines the patient is in a physical exercise state according to the signals from the accelerometer. At block 23, the processer adjusts a series of algorithms depending partly on the signals from the accelerometer. At block 24, the processer forbids a calibration of the glucose sensor for the reason that the calibration result would be unreliable during a fast-changing period of the glucose level. At block 25, the processer excludes the abnormal fluctuation using the adjusted algorithm. At block 26, the processer allows the calibration of the glucose sensor when the abnormal fluctuation is excluded.
The above descriptions of the detailed embodiments are only to illustrate the principle and the effect of the present invention, and it is not to limit the scope of the present invention. Those skilled in the art can modify or change the embodiments without departing from the spirit and scope of the present invention. Accordingly, all equivalent modifications and variations completed by persons of ordinary skill in the art, without departing from the spirit and technical idea of the present invention, should fall within the scope of the present disclosure defined by the appended claims.

Claims (10)

  1. A method for adjusting algorithms in a continuous glucose monitoring (CGM) system, comprising:
    sensing an activity level of a patient by at least one motion sensor set in a CGM system；
    providing signals indicative of the activity level of the patient by the motion sensor to a processer of the CGM system；
    determining the physical state of the patient according to the activity level via the processer；
    adjusting a plurality of algorithms via the processer depending partly on the signals from the motion sensor.
  2. The method according to Claim 1, wherein,
    the motion sensor comprises one or more from an accelerometer, a gyroscope and an attitude sensor.
  3. The method according to Claim 1, wherein,
    further comprises adjusting the algorithms according to different exercise intensities when the patient is in a physical exercise state.
  4. The method according to Claim 1, wherein,
    the algorithms comprise a filtering algorithm to calculate the blood glucose value.
  5. The method according to Claim 1, wherein,
    the algorithms comprise a predictive low glucose algorithm.
  6. The method according to Claim 1, wherein,
    the algorithms comprise an alert threshold algorithm.
  7. The method according to Claim 6, wherein,
    further comprises automatically switching the CGM system into an audio‐off mode for low‐priority alerts that do not require immediate action according to the adjusted algorithm.
  8. The method according to Claim 1, wherein,
    further comprises excluding abnormal fluctuations of the blood glucose sensor data by adjusting related algorithms when the patient is determined in a state of physical exercise by the processer.
  9. The method according to Claim 8, wherein,
    when a calibration of the blood glucose sensor is performed at the same time of an abnormal fluctuation taking place, related algorithm is adjusted by the processer to pause the calibration until the abnormal fluctuation is excluded.
  10. A system using the method for adjusting blood glucose related algorithms according to Claim 1, comprising,
    a CGM system；
    at least one motion sensor set in the CGM system configured to sense the activity levels of a patient and provide corresponding signals；
    a processer set in the CGM system configured to determine the physiological states and exercise intensities of the patient and adjust related algorithms depending partly on the signals from the motion sensor.
PCT/CN2016/113676 2016-12-30 2016-12-30 System and method for algorithm adjustment applying motion sensors in cgm system Ceased WO2018120096A1 (en)

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