FATIGUE MONITORING AND MANAGEMENT SYSTEM

• US 20160270718A1
• Filed: 10/06/2014
• Published: 09/22/2016
• Est. Priority Date: 10/09/2013
• Status: Active Grant

• Current Assignee: ResMed Sensor Technologies Limited (ResMed Incorporated)
• Original Assignee: ResMed Sensor Technologies Limited (ResMed Incorporated)
• Inventors: McCourt, Ciaran Gerard, Shouldice, Redmond, Heneghan, Conor, McMahon, Stephen
• Granted Patent: US 11,197,633 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• 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 5/1118 Determining activity level
• CPC Class Codes: A61B 5/18 for vehicle drivers or machine operators
• CPC Class Codes: A61B 5/291 for electroencephalography [EEG]
• CPC Class Codes: A61B 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/4815 Sleep quality
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/7253 characterised by using transforms
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• 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 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61B 7/003 Detecting lung or respiration noise
• CPC Class Codes: A61M 16/0069 the speed thereof being controlled by respiratory parameters, e.g. by inhalation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment

Abstract

A system monitors fatigue of a user. The system (100) may include one or more data sources, such as a non-obtrusive sleep sensor, configured to generate objective sleep measures of the user. The system may also include a fatigue monitoring module, which may be configured to generate an assessment, such as in one or more processors, of the fatigue state of the user based on the data from the one or more data sources.

Claims

1. A system for monitoring fatigue of a user, the system comprising:

one or more data sources, comprising;

a non-obtrusive sleep sensor configured to generate objective sleep measures of the user; and

a fatigue monitoring module of a processor, the module configured to generate an assessment of a fatigue state of the user based on the data from the one or more data sources.

2. A system according to claim 1, wherein the one or more data sources further comprises one or more of:

an activity sensor configured to generate physical activity data of the user;

an environmental sensor configured to generate environmental data relating to ambient conditions in a sleep location of the user;

a device configured to capture subjective user data related to the user'"'"'s self-perceived fatigue state;

a device configured to capture daytime vital signs data of the user;

a device configured to capture objective measurements of fatigue or sleepiness of the user;

a clock; and

work pattern information for the user.

3. A system according to claim 2, wherein the subjective user data comprises lifestyle parameters comprising one or more of:

caffeine intake;

stress levels;

energy levels;

state of mind; and

perceived sleep quality.

4. A system according to claim 2, wherein the environmental data comprises one or more of season, weather, and allergy information.

5. A system according to claim 2, wherein the environmental data comprises one or more of ambient temperature, ambient audio levels, light levels, air quality, and humidity.

6. A system according to claim 2, wherein the objective measurements of fatigue or sleepiness are obtained from user tests.

7. A system according to claim 2, wherein the objective measurements of fatigue or sleepiness are obtained from game play by the user.

8. A system according to claim 1, wherein the fatigue monitoring module generates the assessment of the fatigue state of the user based on a historical database configured to capture data from the one or more data sources over a predetermined time window.

9. A system according to claim 8, wherein the fatigue monitoring module is further configured to generate the assessment of the fatigue state of the user based on baseline parameters for the user derived from trend analysis of the data in the historical database.

10. A system according to claim 1, wherein the fatigue monitoring module is further configured to generate the assessment of the fatigue state of the user based on a population database comprising data from the one or more data sources from multiple users of the system.

11. A system according to claim 1, wherein the fatigue monitoring module is further configured to generate the assessment of the fatigue state of the user based on baseline parameters for the user derived from responses to a questionnaire.

12. A system according to claim 1, wherein the sleep sensor is further configured to provide a sleep disordered breathing measure.

13. A system according to claim 12, wherein the sleep disordered breathing measure is a snoring measure.

14. A system according to claim 13, wherein the sleep sensor data is combined with audio data from an audio sensor to obtain the snoring measure.

15. A system according to claim 14, wherein the snoring measure is restricted to intervals when the sleep sensor data indicates that the user is present and asleep.

16. A system according to claim 14, wherein the sleep sensor is a movement sensor, and obtaining the snoring measure comprises detecting a snoring-like event in the audio data simultaneous with a high frequency component in a respiratory movement signal from the movement sensor.

17. A system according to claim 12, wherein the sleep disordered breathing measure is an apnea-hypopnea index.

18. A system according to claim 12, wherein the sleep disordered breathing measure is an elevated breathing rate.

19. A system according to claim 1, wherein the assessment of the fatigue state of the user comprises an estimate of a present fatigue state of the user.

20. A system according to claim 1, wherein the assessment of the fatigue state of the user comprises a prediction of a future fatigue state of the user at a specified time.

21. A system according to claim 1, wherein the objective sleep measures comprise one or more of:

heart rate;

breathing rate;

biomotion levels;

sleep statistics;

galvanic skin response; and

body temperature.

22. A system according to claim 21, wherein the sleep statistics comprise one or more of:

duration of sleep;

quality of sleep;

number of interruptions of sleep;

REM sleep duration;

wake after sleep onset;

sleep inertia; and

sleep latency.

23. A system according to claim 1, further comprising a third party information module configured to provide information to a third party related to the assessment of the fatigue state of the user.

24. A system according to claim 1, further comprising a user information module configured to provide information to the user related to the assessment of the fatigue state of the user.

25. A system according to claim 1, wherein the sleep sensor is a sensor integrated with a respiratory pressure therapy device from which the user is receiving CPAP therapy.

26. A system according to claim 1, wherein the fatigue monitoring module is a linear classifier that is configured to linearly combine the data from the one or more data sources to generate a fatigue index.

27. A system according to claim 1, wherein the fatigue monitoring module applies a rule set to the data from the one or more data sources to generate a fatigue index.

28. A system according to claim 26, wherein the fatigue index is mapped to one of set of fatigue states.

29. A system according to claim 1, wherein the fatigue monitoring module is implemented on a processing device associated with the user, the processing device being connected to the one or more data sources.

30. A system according to claim 1, wherein the fatigue monitoring module is implemented at a remote server connected to the one or more data sources over a network.

40. A system according to claim 22, wherein the sleep statistics comprise REM sleep duration, and the assessment of the fatigue state of the user is an assessment of the mental fatigue state of the user.

41. A system according to claim 22, wherein the sleep statistics comprise deep sleep duration, and the assessment of the fatigue state of the user is an assessment of the physical fatigue state of the user.

42. A system according to claim 1, wherein the assessment of the fatigue state of the user is a sleep age of the user.

43. A system according to claim 2, wherein the subjective user data comprises responses to a STOP-BANG questionnaire.

44. A system according to claim 28, wherein one of the fatigue states is acute fatigue.

45. A system according to claim 28, wherein one of the fatigue states is chronic fatigue.

46. A system according to claim 1, wherein the one or more data sources further comprises information from the user on conditions/disease states that are related to fatigue.

47. A system according to claim 1, wherein the objective sleep measures further comprise heart rate variability (HRV).

48. A system according to claim 2, wherein the device configured to capture daytime vital signs data of the user is an ECG data collection device.

49. A system according to claim 2, wherein the device configured to capture objective measurements of fatigue or sleepiness of the user is an EEG electrode.

50. A system according to claim 26, wherein the classifier is a linear classifier.

51. A system according to claim 26, wherein the classifier is a non-linear classifier.

52. A system according to claim 1, wherein the fatigue monitoring module is a neural network.

53. A system according to claim 1, wherein the fatigue monitoring module is a support vector machine.

31. A method of monitoring fatigue of a user, the method comprising generating, in one or more processors, an assessment of a fatigue state of the user based on data from one or more data sources, the data comprising objective sleep measures of the user generated by a non-obtrusive sleep sensor.

32. A method according to claim 31, further comprising providing the user with the fatigue state assessment.

33. A method according to claim 31, further comprising making a recommendation to the user based on the fatigue state assessment.

34. A method according to claim 33, wherein the recommendation is an ideal time for the user to go to sleep.

35. A method according to claim 33, wherein the recommendation is an optimal time for the user to wake up.

36. A method according to claim 33, wherein the user is undergoing CPAP therapy, and the recommendation is a recommendation to improve the CPAP therapy.

37. A method according to claim 31, further comprising providing a third party with the fatigue state assessment.

38. A method according to claim 31, wherein the generating comprises applying a linear classifier to the one or more data sources.

39. A method according to claim 38, further comprising, before the generating, applying a non-linear transformation to one or more of the data sources.

54. A method according to claim 33, wherein the recommendation is for the user to seek alternative means of transport.

55. A method according to claim 31, further comprising issuing an alert to the user based on the fatigue state assessment.

56. A method according to claim 55, wherein the alert is a prescriptive alert.

57. A method according to claim 31, further comprising providing guided respiration to the user based on the fatigue state assessment.

58. A method according to claim 33, wherein the recommendation is to listen to a music track at go-to-sleep time.

59. A method according to claim 38, further comprising training the classifier using objective measurements of fatigue as a proxy for the fatigue state of the user.

60. A method according to claim 59, wherein the objective measurements of fatigue were obtained from the user.

61. A method according to claim 59, wherein the objective measurements of fatigue were obtained from multiple individuals.

62. A method according to claim 38, further comprising training the classifier using subjective user data related to the user'"'"'s self-perceived fatigue state as a proxy for the fatigue state of the user.

63. A method according to claim 38, wherein the classifier is a linear classifier.

64. A method according to claim 38, wherein the classifier is a non-linear classifier.

65. A method according to claim 31, wherein the generating comprises applying a neural network to the one or more data sources.

66. A method according to claim 31, wherein the generating comprises applying a support vector machine to the one or more data sources.