DETECTION AND IDENTIFICATION OF A HUMAN FROM CHARACTERISTIC SIGNALS

• US 20180106897A1
• Filed: 04/20/2016
• Published: 04/19/2018
• Est. Priority Date: 04/20/2015
• Status: Active Grant

• Current Assignee: ResMed Sensor Technologies Limited (ResMed Incorporated)
• Original Assignee: ResMed Sensor Technologies Limited (ResMed Incorporated)
• Inventors: Shouldice, Redmond, DOHENY, Emer, Zaffaroni, Alberto
• Granted Patent: US 10,690,763 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0507 using microwaves or terahertz waves
• CPC Class Codes: A61B 5/0533 Measuring galvanic skin response
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/1102 Ballistocardiography
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/117 Identification of persons methods or arrangements for recognising patterns, e.g. fingerprints, G06F18/00, G06V40/00; identification of persons by analysing their voice or speech G10L17/00
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/4836 Diagnosis combined with treatment in closed-loop systems or methods A61B5/0036 takes precedence
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 7/003 Detecting lung or respiration noise
• CPC Class Codes: A61M 16/0051 with alarm devices
• CPC Class Codes: A61M 16/0069 the speed thereof being controlled by respiratory parameters, e.g. by inhalation
• CPC Class Codes: A61M 16/0072 Tidal volume piston pumps
• CPC Class Codes: A61M 16/026 : specially adapted for predicting, e.g. for determining an information representative of a flow limitation during a ventilation cycle by using a root square technique or a regression analysis
• CPC Class Codes: A61M 16/0683 : Holding devices therefor
• CPC Class Codes: A61M 16/16 : Devices to humidify the respiration air A61M16/1045 takes precedence
• CPC Class Codes: A61M 2016/0027 : pressure meter
• CPC Class Codes: A61M 2016/0033 : electrical
• CPC Class Codes: A61M 2202/0208 : Oxygen
• CPC Class Codes: A61M 2205/21 : insensitive to tilting or inclination, e.g. spill-over prevention
• CPC Class Codes: A61M 2205/3553 : remote, e.g. between patient's home and doctor's office
• CPC Class Codes: A61M 2205/502 : User interfaces, e.g. screens or keyboards
• CPC Class Codes: A61M 2205/6009 : for matching patient with his treatment, e.g. to improve transfusion security
• CPC Class Codes: A61M 2205/6018 : providing set-up signals for the apparatus configuration
• CPC Class Codes: A61M 2205/609 : Biometric patient identification means
• CPC Class Codes: G01S 13/56 : for presence detection presence detection using near field arrangements G01V3/00, e.g. G01V3/08, G01V3/12; burglar, theft or intruder alarms with electrical actuation G08B13/22 - G08B13/26
• CPC Class Codes: G01S 13/87 : Combinations of radar systems, e.g. primary radar and secondary radar
• CPC Class Codes: G01S 13/88 : Radar or analogous systems specially adapted for specific applications electromagnetic prospecting or detecting of objects, e.g. near-field detection, G01V3/00
• CPC Class Codes: G01S 7/415 : Identification of targets based on measurements of movement associated with the target
• CPC Class Codes: G06V 40/00 : Recognition of biometric, human-related or animal-related patterns in image or video data
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: Y02A 90/10 : Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

One or more sensors are configured for detection of characteristics of moving objects and living subjects for human identification or authentication. One or more processors, such as in a system of sensors or that control a sensor, may be configured to process signals from the one or more sensors to identify a person. The processing may include evaluating features from the signals such as breathing rate, respiration depth, degree of movement and heart rate etc. The sensors may be radio frequency non-contact sensors with automated detection control to change detection control parameters based on the identification of living beings, such as to avoid sensor interference.

Claims

1. A system to identify a person for monitoring physiological parameters of one or more persons, the system including:

one or more sensors for monitoring the one or more persons'"'"' physiological parameters, and

one or more processors, the one or more processors configured to process signals from the one or more sensors to identify a person, the processing comprising an evaluation of features comprising one or more respiratory features, cardiac features, or movement features.

2. The system of claim 1 wherein the evaluation of features comprises analysis of at least one of:

detected breathing rate, detected respiration depth, detected degree of movement and detected heart rate.

3. The system of any one of claims 1 to 2 wherein the one or more sensors comprises a radio frequency non-contact sensor.

4. The system of claim 2, wherein the monitoring comprises detection of physiological characteristics of the person during sleep.

5. The system of any one of claims 1 to 4 wherein the processing comprises detection of sleep stages of the person.

6. The system of any one of claims 1 to 5 wherein the processing comprises detection of deep sleep of the person.

7. The system of any one of claims 1 to 6 3 the processing comprises detection of REM sleep of the person.

8. The system of any one of claims 1 to 7 wherein the monitoring comprises detection of physiological characteristics during awake time of the person.

9. The system of any one of claims 1 to 8 wherein the one or more sensors comprises any one or more of:

a radio frequency non-contact sensor;

a biomotion sensor; and

a wearable sensor.

10. The system of any one of claims 1 to 9 wherein the evaluation of the one or more processors comprises detection and analysis of any one or more characteristics comprising biometric and behavioural characteristics.

11. The system of any one of claims 1 to 10 wherein the one or more sensors are configured to minimize interference between at least two of the one or more sensors.

12. The system of any one of claims 1 to 11 wherein the one or more sensors minimize interference by modifying control parameters for range gating pulse timing, emitted power levels of pulses, detection frequency of pulses and/or adjusting positioning of a steerable antenna.

13. The system of any one of claims 1 to 12 wherein the one or more sensors communicate via a wired or wireless link.

14. The system of any one of claims 1 to 13 wherein characteristic biometric parameters are applied to dynamically adjust performance of at least one of the one or more sensors in order to optimize physiological recognition of independent human sources, and to reject other sources.

15. The system of any one of claims 1 to 14 further comprising a control processor in communication with the one or more sensors, the control processor communicating with the one or more sensors to adjust a detection control parameter of the one or more sensors based on an identification of the person, other people, or an animal by the one or more sensors.

16. The system of claim 15 wherein the detection control parameters of the one or more sensors comprises one or more of range gating, centre frequency, and power level.

17. The system of any one of claims 1 to 17 further comprising a control processor in communication with the one or more sensors, the control processor configured to activate a further sensor system based on an identification of the person, other people, or an animal by the one or more sensors.

18. The system of claim 17 wherein the further sensor system comprises a camera.

19. The system of any one of claims 1 to 18 wherein a processor logs detected biometric characteristics based on the identification of the person to be a previously monitored person.

20. The system of any one of claims 1 to 19 wherein a processor refrains from logging detected biometric characteristics based on the identification of the person to be a not previously monitored person.

21. The system of any one of claims 1 to 20 wherein a processor is configured to initialize biometric characteristic detection for a particular person.

22. The system of claim 21 wherein the processor compares newly detected biometric characteristics to initialized biometric characteristics to identify the particular person.

23. The system of claim 21 wherein the evaluation further comprises a comparison between newly detected biometric characteristics and initialized biometric characteristics.

24. The system of any one of claims 1 to 23 wherein the evaluation comprises classification of features determined from the signals, the features comprising one or more of:

a spectral peak ratio;

a set up Optimiser flag vector;

a peak trough ratio;

a filtered respiration rate;

a breathing variability measure;

an in-band power of a sensor signal;

a range of a sensor signal;

a final respiration rate;

a ratio of maximum to minimum amplitude of a breathing cycle;

a high band power for a sensor signal;

a mean respiration rate;

a periodic leg movement activity detection;

a detection of turnover; and

a post-processed movement.

25. The system of any one of claims 1 to 23 wherein the evaluation comprises classification of features determined from the signals, the features comprising one or more of a cardiac parameter, a galvanic skin response parameter, an exercise intensity parameter, a respiration parameter, a blood pressure parameter, a coughing parameter, a snoring parameter, a sleep parameter.

26. The system of any one of claims 24 to 25 wherein the evaluation comprises a comparison of the determined features with historic features.

27. The system of any one of claims 24 to 26 wherein the evaluation further comprises calculating mean and/or standard deviation values for a period of time from the determined features.

28. The system of any one of claims 1 to 27 wherein one or more processors of the system evaluates received data detected by one or more sensors from another monitoring system.

29. The system of claim 28 wherein the evaluation of the received data comprise a determination of sensing equivalence between a plurality of health monitoring devices.

30. The system of any one of claims 1 to 29 wherein a monitoring device comprises a setup classifier and a subject classifier, each configured to evaluate features from the signals to identify a person.

31. The system of claim 30 wherein the subject classifier is further configured to evaluate historic features to identify a person.

32. The system of any one of claim 1 to 31 wherein a health monitoring device further comprises a camera sensitive to infra-red and an infra-red emitter.

33. The system of claim 32 wherein one or more processors of the health monitoring device are configured to detect an event from the signals and associate the event with a particular portion of a video taken with the camera including the event.

34. The system of any one of claims 32 to 33 wherein the health monitoring device further comprises a battery and a coil for wireless charging of the battery.

35. The system of any one of claims 1 to 34 wherein the one or more processors is configured to control the one or more sensors to change sensor detection power, sensor frequency, sensor range gating or other control parameters for sensing, upon detection of biometric characteristics indicative of an animal.

36. The system of any one of claims 1 to 34 wherein the one or more processors is configured to access setting parameters for any one or more of light, sound and/or environmental appliances based on identification of a specific person associated with the setting parameters.

37. The system of claim 36 wherein the one or more processors is configured to trigger setting of any one or more of light, sound and/or environmental appliances with setting parameters associated with detected biometrics of the specific person identified.

38. The system of any one of claims 1 to 37 further comprising a central controller in communication with a collection of said sensors, said central controller configured to provide confirmation of location of identified persons within a structure.

39. The system of any one of claims 1 to 38 wherein the one or more sensors comprises a night light.

40. The system of any one of claims 1 to 39 wherein the one or more sensors comprises an pass through outlet.

41. The system of any one of claims 1 to 40 wherein the one or more sensors comprises an AC plug for powering the sensor and an AC power pass through outlet.

42. The system of any one of claims 1 to 41 wherein the one or more sensors comprises an USB plug for powering the sensor and a USB pass through connection adapter.

43. The system of any one of claims 1 to 42 wherein the one or more sensors comprises a network interface for wireless or wired network communications.

44. The system of any one of claims 1 to 43 wherein the one or more processors is configured to set operation of a respiratory treatment apparatus based on the person identified.

45. The system of claim 44 wherein the set operation permits or denies therapy with the respiratory treatment apparatus.

46. The system of claim 44 wherein the set operation changes therapy of the respiratory treatment apparatus.

47. The system of any one of claims 44 to 46 wherein the one or more processors is configured to re-train for identification of the person if biometric characteristics evaluated in the identification are treated by the respiratory treatment apparatus.

48. The system of any one of claims 1 to 47 wherein the one or more processors is configured to adjust operation of the one or more sensors upon determination of sensor signal quality.

49. The system of any one of claims 1 to 48 wherein the one or more processors is configured to rely on different biometric characteristics to identify the person depending on a quality assessment of detected biometric characteristics.

50. The system of any one of claims 1 to 49 wherein the one or more processors is configured to operate an enrolment process for initialization of a baseline of biometric characteristics for identifying the person, wherein the enrollment process comprises a guided breathing session or spontaneous breathing session.

51. The system of any one of claims 1 to 50 wherein the one or more processors is configured to reject a biometric characteristic involved in identifying the person when frequency interference is detected.

52. The system of any one of claims 1 to 51 wherein the one or more processors is configured to set operation of an alarm based on the person identified.

53. The system of any one of claims 1 to 52 wherein the one or more processors is configured to identify a main user from one or more other users.

54. The system of any one of claims 1 to 53, wherein the system is configured to track parameters of one or more users over time to build up classification features based on at least one of the one or more respiratory features, cardiac features, or movement features.

55. The system of claim 54, wherein at least one of the one or more respiratory features, cardiac features, or movement features includes at least one of the following respiration parameters:

range, breath to breath variation, shape and inspiration verses expiration ratio.

56. The system of any one of claims 1 to 55 wherein the one or more processors is configured to classify a user'"'"'s identity from features determined in a classification process.

57. The system of claim 56 wherein the classification process comprises any one or more of a neural network, a hidden layer Markov model, logistic regression processing, linear kernel support vector machine, and radial kernel support vector machine.

58. The system of any one of claims 56 to 57 wherein the classification process comprises using Principal Component Analysis on the features prior to classification.

59. The system of any one of claims 56 to 58 wherein the classification process includes real time features and offline features.

60. The system of any one of claims 56 to 59 wherein the classification process includes multiple classifiers and late integration of their output to produce an output posterior probability.

61. The system of any one of claims 1 to 60, comprising multiple sensors arranged for detecting a same or different persons, wherein the system is arranged to automatically adjust parameters of the multiple sensors, the parameters including at least one of:

range, power, frequency, detection direction and radiation pattern.

62. A method to identify a person for monitoring physiological parameters of one or more persons, the method comprising:

monitoring, with one or more sensors for sensing, the one or more persons'"'"' physiological parameters, and

processing in one or more processors, signals from the one or more sensors to identify a person, the processing comprising evaluating features comprising one or more respiratory features, cardiac features, or movement features.

63. The method of claim 62 wherein the evaluating of features comprises analysis of at least one of:

detected breathing rate, detected respiration depth, detected degree of movement and detected heart rate.

64. The method of any one of claims 62 to 63 wherein the one or more sensors comprises a radio frequency non-contact sensor.

65. The method of any one of claims 62 to 64 wherein the evaluating comprises detection and analysis of any one or more characteristics comprising biometric and behavioural characteristics.

66. The method of any one of claims 62 to 65 further comprising, with a control processor in communication with the one or more sensors, activating a further sensor system based on an identification of the person, other people, or an animal by the one or more sensors.

67. The method of any one of claims 62 to 66 further comprising, with a processor, logging detected biometric characteristics based on the identification of the person to be a previously monitored person.

68. The method of any one of claims 62 to 67 further comprising, with a processor, refraining from logging detected biometric characteristics based on the identification of the person to be a not previously monitored person.

69. The method of any one of claims 62 to 68 further comprising initializing biometric characteristic detection for a particular person.

70. The method of claim 69 further comprising, in a processor, comparing newly detected biometric characteristics to initialized biometric characteristics to identify the particular person.

71. The method of claim 69 further comprising, in a processor, comparing newly detected biometric characteristics and initialized biometric characteristics.

72. The method of any one of claims 62 to 71 wherein the evaluating comprises classifying features determined from the signals, the features comprising one or more of:

a spectral peak ratio;

a set up Optimiser flag vector;

a peak trough ratio;

a filtered respiration rate;

a breathing variability measure;

an in-band power of a sensor signal;

a range of a sensor signal;

a final respiration rate;

a ratio of maximum to minimum amplitude of a breathing cycle;

a high band power for a sensor signal;

a mean respiration rate;

a periodic leg movement activity detection;

a detection of turnover; and

a post-processed movement.

73. The method of any one of claims 62 to 72 wherein the evaluating comprises classifying features determined from the signals, the features comprising one or more of a cardiac parameter, a galvanic skin response parameter, an exercise intensity parameter, a respiration parameter, a blood pressure parameter, a coughing parameter, a snoring parameter, a sleep parameter.

74. The method of any one of claims 72 to 73 wherein the evaluating comprises comparing the determined features with historic features.

75. The method of any one of claims 72 to 74 wherein the evaluating further comprises calculating, in a processor, mean and/or standard deviation values for a period of time from the determined features.

76. The method of any one of claims 62 to 75 further comprising, with the one or more processors, controlling the one or more sensors to change sensor detection power, sensor frequency, sensor range gating or other control parameters for sensing, upon detection of biometric characteristics indicative of an animal.

77. The method of any one of claims 62 to 75 further comprising, with the one or more processors, accessing setting parameters for any one or more of light, sound and/or environmental appliances based on identification of a specific person associated with the setting parameters.

78. The method of claim 77 further comprising, with the one or more processors, triggering setting of any one or more of light, sound and/or environmental appliances with setting parameters associated with detected biometrics of the specific person identified.

79. The method of any one of claims 62 to 78 further comprising, with one or more processors, setting operation of a respiratory treatment apparatus based on the person identified.

80. The method of claim 79 wherein the setting operation permits or denies therapy with the respiratory treatment apparatus.

81. The method of claim 79 wherein the setting operation changes therapy of the respiratory treatment apparatus.

82. The method of any one of claims 62 to 81, further comprising, with the one or more processors, operating an enrolment process for initialization of a baseline of biometric characteristics for identifying the person, wherein the enrollment process comprises a guided breathing session or spontaneous breathing session.

83. The method of any one of claims 62 to 82 further comprising, with the one or more processors, setting operation of an alarm based on the person identified.

84. The method of any one of claims 62 to 83 further comprising, in the one or more processors, is configured to identifying a main user from one or more other users.

85. The method of any one of claims 62 to 84, further comprising tracking parameters of one or more users over time and building classification features based on at least one of the one or more respiratory features, cardiac features, or movement features.

86. The method of claim 85, wherein at least one of the one or more respiratory features, cardiac features, or movement features includes at least one of the following respiration parameters:

range, breath to breath variation, shape and inspiration verses expiration ratio.

87. The method of any one of claims 62 to 86 further comprising, in the one or more processors, classifying a user'"'"'s identity from features determined in a classification process.

88. The method of claim 87 wherein the classification process comprises any one or more of a neural network, a hidden layer Markov model, logistic regression processing, linear kernel support vector machine, and radial kernel support vector machine.

89. The method of any one of claims 87 to 88 wherein the classification process comprises using Principal Component Analysis on the features prior to classification.

90. The method of any one of claims 88 to 89 wherein the classification process includes real time features and offline features.

91. The method of any one of claims 88 to 90 wherein the classification process includes multiple classifiers and late integration of their output to produce an output posterior probability.

92. The method of any one of claims 62 to 91, further comprising, with multiple sensors arranged for detecting a same or different persons, automatically adjusting parameters of the multiple sensors, the parameters including at least one of:

range, power, frequency, detection direction and radiation pattern.