Systems, methods, and apparatus for monitoring alertness of an individual utilizing a wearable device and providing notification

• US 9,905,108 B2
• Filed: 09/09/2015
• Issued: 02/27/2018
• Est. Priority Date: 09/09/2014
• Status: Active Grant

• Current Assignee: Torvec, Inc.
• Original Assignee: Torvec, Inc.
• Inventors: Kaplan, Richard A., Hemink, Douglas A., Kenyon, Matt
• Primary Examiner(s): Pope, Daryl
• Application Number:
  US14/848,771
• Publication Number:
  US 20160071393A1
• Time in Patent Office: 902 Days
• Field of Search: 3405391, 34053911, 34053913, 3405731
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• 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 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/0533 Measuring galvanic skin response
• CPC Class Codes: A61B 5/1104 induced by stimuli or drugs; A61B5/1102 takes precedence; investigating medicinal preparations G01N33/15
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/162 Testing reaction times
• CPC Class Codes: A61B 5/18 for vehicle drivers or machine operators
• CPC Class Codes: A61B 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/6831 Straps, bands or harnesses
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/74 Details of notification to user or communication with user or patient indicating measured values G01D7/00; user input means input or output arrangements for computers G06F3/00
• CPC Class Codes: A61B 5/7405 using sound
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7455 characterised by tactile indication, e.g. vibration or electrical stimulation
• CPC Class Codes: B60K 28/066 actuating a signalling device B60K28/063 takes precedence
• CPC Class Codes: B60W 40/08 related to drivers or passengers
• CPC Class Codes: G08B 21/043 : detecting an emergency event, e.g. a fall
• CPC Class Codes: G08B 21/0446 : worn on the body to detect changes of posture, e.g. a fall, inclination, acceleration, gait
• CPC Class Codes: G08B 21/0453 : worn on the body to detect health condition by physiological monitoring, e.g. electrocardiogram, temperature, breathing detecting, measuring or recording for diagnostic purposes A61B5/00
• CPC Class Codes: G08B 21/06 : indicating a condition of sleep, e.g. anti-dozing alarms
• CPC Class Codes: G08B 25/016 : Personal emergency signalling and security systems emergency non-personal manually actuated alarm activators G08B25/12
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass

Abstract

Methods, systems, and apparatus for monitoring fatigue and notifying an individual are described. The individual may be an operator of a vehicle, equipment, or machine, a student, or other person that may experience fatigue. In addition to the individual, another person such as an employer, teacher, or parent may be notified.

Claims

1. A wearable apparatus for monitoring fatigue and notifying a user, the wearable apparatus comprising:

a motion sensor configured to monitor motion of the user;

a notification generator configured to issue a stimulus and to generate a warning to the user in response to receipt of a signal;

a processor coupled to the motion sensor and the notification generator, the processor configured to;

obtain a base responsiveness profile of the user for a prescribed motion;

generate a current responsiveness profile of the user for the prescribed motion from the monitored motion of the user in response to the stimulus, wherein the monitored motion includes the prescribed motion;

determine whether the user response to the stimulus exceeds a predetermined threshold of the base responsiveness profile by comparing the current responsiveness profile to the base responsiveness profile; and

send the signal to the notification generator when the user response exceeds the predetermined threshold of the base responsiveness profile; and

a support configured to support the motion sensor, notification generator, and processor on the user.

2. The wearable apparatus of claim 1, wherein the processor is further configured to:

compare the motion monitored by the motion sensor to a predetermined threshold of a parameter of the prescribed motion, and

generate the signal to the notification generator when the predetermined threshold of the parameter is exceeded.

3. The wearable apparatus of claim 2, wherein the parameter is a lack of motion detected for a specified period of time.

4. The wearable apparatus of claim 1, wherein the support is a band.

5. The wearable apparatus of claim 1, wherein the wearable apparatus is further configured to monitor biomarkers of the user.

6. The wearable apparatus of claim 5, wherein the wearable apparatus further comprises a biometric sensor module coupled to the processor, wherein the biometric sensor module is configured to monitor the biomarkers of the user and includes at least one of:

a skin temperature sensor configured to detect distal skin temperature of the user;

a sensor configured to detect pulse and heart rate intervals (R-R) of the user;

a Light Emitting Diode (LED) and a photodiode in combination configured to detect photoplethysmography (PPG) data on blood volume of the user;

an electrocardiogram (EKG) configured to measure heart-related metrics of the user;

a proximity sensor configured to detect when the user is wearing the wearable apparatus;

a capacitive touch sensor configured to detect when the user is wearing the wearable apparatus;

or at least one electrode configured to measure skin conductivity; and

wherein the processor is further configured to process the biomarkers of the user monitored by the biometric sensor module.

7. The wearable apparatus of claim 1, wherein the wearable apparatus further comprises a Global Positioning System (GPS) coupled to the processor configured to:

determine a location of the user, and

transmit, via an associated wireless connection, the location of the user

to a third party; and

wherein the processor is further configured to process outputs from the GPS.

8. The wearable apparatus of claim 1, wherein the wearable apparatus further comprises, coupled to the processor, at least one of:

an ambient temperature sensor configured to measure environmental temperature;

a light sensor configured to measure exposure of the user to ambient light;

or a clock configured to monitor timed events; and

wherein the processor is configured to process outputs from the at least one of the ambient temperature sensor, the light sensor, or the clock.

9. The wearable apparatus of claim 5, wherein the support is configured to be worn on a body part of the user.

10. The wearable apparatus of claim 9, wherein the wearable apparatus further comprises a biometric sensor module coupled to the processor, wherein the biometric sensor module is configured to monitor the biomarkers of the user and includes a power-measuring device configured to measure a power exerted by the body part of the user;

and

wherein the processor is further configured to process the biomarkers of the user monitored by the biometric sensor module.

11. The wearable apparatus of claim 10, wherein the power-measuring device includes at least one of a strain gauge, a spring, a piezoelectric strain gauge, or an elastic band.

12. The wearable apparatus of claim 1, wherein the notification generator comprises at least one of an audible tone generator, a visual generator, or a vibration generator.

13. The wearable apparatus of claim 1, wherein the motion sensor includes at least one of a gyroscope or an accelerometer.

14. The wearable apparatus of claim 1, wherein the prescribed motion comprises rotation of a wrist of the user.

15. The wearable apparatus of claim 1, wherein the processor is further configured to calculate a stimulus interval for stimulating the user to perform the prescribed motion.

16. The wearable apparatus of claim 1, wherein the wearable apparatus is further configured to monitor sleep risk variables.

17. The wearable apparatus of claim 16, wherein the wearable apparatus is configured to monitor the sleep risk variables during an initial testing period;

and

wherein the processor is further configured to;

process sleep risk variables during an initial testing period, and

generate a baseline fatigue risk level for the user from the sleep risk variables monitored during an initial testing period.

18. The wearable apparatus of claim 1, wherein the processor is further configured to:

store at least one current responsiveness profile as a historical responsiveness profile;

determine whether the historical responsiveness profile exceeds the

predetermined threshold of the base responsiveness profile by

comparing the historical responsiveness profile to the base responsiveness profile; and

send the signal to the notification generator when the historical responsiveness profile exceeds the predetermined threshold of the base responsiveness profile.

19. The wearable apparatus of claim 17, wherein the wearable apparatus is further configured to continually monitor the sleep risk variables of the user following the initial test period.

20. The wearable apparatus of claim 17, wherein the processor is further configured to generate dynamic risk levels of the user.

21. The wearable apparatus of claim 16, wherein the sleep risk variables include at least one of:

motion outputs monitored by the motion sensor, biomarkers of the user monitored by a biometric sensor module, a location of the user within the user'"'"'s personal circadian rhythm, subjective alertness levels input by the user, a sleep drive of the user, the current responsiveness profile, data derived from a chronotype input of the user, or a body position of the user.

22. The wearable apparatus of claim 17, wherein the processor is further configured to calculate a stimulus interval for stimulating the user to perform the prescribed motion by incorporating the baseline fatigue risk level.

23. The wearable apparatus of claim 2, wherein the parameter of the prescribed motion is one of total time, reaction time, time of motion, velocity, acceleration, jerk, or range of motion.

24. The wearable apparatus of claim 6, wherein the biometric sensor module includes a light sensor configured to measure exposure of the user to ambient light.

25. The wearable apparatus of claim 6, wherein at least a component of the biometric sensor module or the motion sensor is configured to detect when the user is wearing the wearable apparatus.

26. The wearable apparatus of claim 1, wherein the notification generator is further configured to transmit a notification to a third party via a transceiver coupled to the wearable apparatus.

27. A method of monitoring alertness of a user and notifying the user, comprising:

obtaining, with a wearable apparatus configured to measure parameters of a prescribed motion, a base responsiveness profile of the user based on the parameters of the prescribed motion;

issuing, with a notification generator, a stimulus to the user to perform the prescribed motion;

monitoring, with the wearable apparatus, a response of the user to the stimulus;

generating a current responsiveness profile from the user response to the stimulus;

determining whether the user response to the stimulus exceeds a predetermined threshold of the base responsiveness profile by comparing the current responsiveness profile to the base responsiveness profile; and

generating an alarm to alert the user, with the notification generator, when the response exceeds the predetermined threshold.

28. The method of claim 27, wherein the step of generating the alarm is performed by a third party.

29. The method of claim 27, wherein the prescribed motion comprises rotation of a wrist of the user.

30. The method of claim 27, wherein the parameters of the prescribed motion include at least one of total time, reaction time, time of motion, velocity, acceleration, jerk, or range of motion.

31. The method of claim 27, wherein the method further comprises calculating, with the wearable apparatus, a stimulus interval for issuing the stimulus to the user based on the base responsiveness profile.

32. The method of claim 31, wherein the method further comprises storing, with the wearable apparatus, at least one of the current responsiveness profiles as an historical responsiveness profile.

33. The method of claim 32, wherein the method further comprises generating, with the wearable apparatus, a responsiveness trend line based on at least one stored historical responsiveness profile.

34. The method of claim 33, wherein the method further comprises:

determining if the responsiveness trend line falls outside a predetermined trend line threshold for responsiveness; and

generating an alarm if the responsiveness trend line falls outside the predetermined trend line threshold.

35. The method of claim 32, wherein the method further comprises:

measuring, with the wearable apparatus, at least one sleep risk variable of the user; and

generating a baseline fatigue risk level of the user based on the at least one measured sleep risk variable, wherein the stimulus interval is calculated based further on the baseline fatigue risk level of the user.

36. The method of claim 35, wherein the method further comprises:

periodically measuring, with the wearable apparatus, the at least one sleep risk variable over time after generating a baseline fatigue risk level;

detecting, with the wearable apparatus, changes in the at least one sleep risk variable over time;

generating, with the wearable apparatus, dynamic risk levels of the user falling asleep based on the changes in the at least one periodically measured sleep risk variable; and

adjusting, with the wearable apparatus, the calculated stimulus interval based on the dynamic risk levels.

37. The method of claim 32, wherein the method further comprises:

comparing the current responsiveness profile to the stored historical responsiveness profile immediately preceding the current responsiveness profile;

detecting if a significant deviation between the current responsiveness profile and the stored historical responsiveness profile exists; and

generating an alarm to the user if a significant deviation is detected.

38. The method of claim 35, wherein the at least one sleep risk variable comprises at least one of:

a location of the user within an estimated personal circadian rhythm of the user,

biomarkers of the user,

subjective alertness levels of the user,

health information of the user,

sleep drive of the user,

a current responsiveness profile for responses of the user to each stimulus,

an historical responsiveness profile,

data derived from a chronotype input of the user,

movements by the user, including the prescribed motion, or

body position of the user.

39. The method of claim 38, wherein the estimated personal circadian rhythm is determined from measurements of at least one of the following biomarkers of the user:

skin temperature,

heart-related metrics, or

galvanic skin response.

40. The method of claim 39, wherein generating the baseline fatigue risk level includes incorporating the estimated personal circadian rhythm of the user.

41. The method of claim 40, wherein the method further comprises:

determining a location of the user within the personal circadian rhythm of the user based on the detected changes in the biomarkers of the user over time;

estimating, based on the location of the user within the estimated personal circadian rhythm, an alertness level of the user; and

incorporating the estimated alertness level into generating dynamic risk levels of the user.

42. The method of claim 41, wherein the step of determining the location of the user within the estimated personal circadian rhythm based on the detected changes in the biomarkers of the user over time includes the detected changes in at least one of the following:

skin temperature,

heart rate, or

galvanic skin response.

43. The method of claim 41, wherein the method further comprises the steps of:

detecting an exposure of the user to light;

determining an effect of the detected exposure to light on the estimated personal circadian rhythm of the user; and

incorporating the detected exposure to light into determining the location of the user within the estimated personal circadian rhythm.

44. The method of claim 35, wherein the method further comprises:

receiving a subject alertness level from the user, and

incorporating the subjective alertness level into the generating a baseline fatigue risk level.

45. The method of claim 36, wherein the method further comprises:

receiving a subjective alertness level from the user, and

incorporating the subjective alertness level into the generating dynamic risk levels.

46. The method of claim 38, wherein the movements of the user and the response of the user to the stimulus are measured by a motion sensor coupled to the wearable apparatus.

47. The method of claim 38, wherein the biomarkers comprise at least one of the following:

skin temperature,

skin resistivity,

skin conductivity,

EOG,

ECG,

EKG, or

heart-related metrics.

48. The method of claim 47, wherein the heart-related metrics comprise at least one of:

heart rate variability (HRV), heart rate intervals (RR), beats per minute, High Frequency (HF) of the RR, Low Frequency (LF) of the RR, blood volume, time domain, or frequency domain.

49. The method of claim 47, wherein the method further comprises the steps of:

measuring, with an ambient temperature detector, a temperature of an environment of the user, and

adjusting any detected changes in skin temperature of the user with the measured temperature of the environment of the user.

50. The method of claim 36, wherein the method further comprises incorporating at least one stored historical responsiveness level into the generating dynamic risk levels of the user falling asleep.

51. The method of claim 50, wherein the method further comprises generating an alertness scale, generating a position of the user on the alertness scale, and displaying the generated position and the alertness scale to the user such that the user is provided with an estimated sleep risk.

52. The method of claim 38, wherein the step of generating a baseline fatigue risk level of the user for falling asleep further comprises:

receiving the data derived from the chronotype input of the user; and

incorporating the data derived from the chronotype input of the user into the generating of the baseline fatigue risk level of the user.

53. The method of claim 36, wherein the generating the dynamic risk levels of the user falling asleep further comprises incorporating data derived from a chronotype input of the user.

54. The method of claim 36, wherein the method further comprises:

measuring, with an ambient temperature detector, a temperature of an environment of the user;

calculating an effect of the temperature of the user'"'"'s environment on the baseline fatigue risk level for falling asleep; and

adjusting the dynamic risk levels of the user falling asleep based on the calculated effect of the temperature of the environment.

55. The method of claim 36, wherein the method further comprises the steps of:

identifying periods where the user is asleep by using the detected changes in the sleep risk variables;

identifying periods where the user is awake by using the detected changes in the sleep risk variables; and

adjusting dynamic risk levels of the user falling asleep based on the identified periods where the user is asleep and where the user is awake.

56. The method of claim 52, wherein the method further comprises determining an amount of time that has passed since the user last slept from the identified periods where the user is asleep and where the user is awake by using at least one of actigraphy techniques or input from the user.

57. The method of claim 36, wherein the method further comprises:

determining a time of day or night, and

adjusting dynamic risk levels of the user falling asleep based on the determined time of day or night.

58. The method of claim 27, wherein the stimulus alerting the user is variable in intensity over a duration of the stimulus.

59. The method of claim 55, wherein the method further comprises identifying periods where the user is asleep by using actigraphy data derived from the measured movements of the user.

60. The method of claim 59, wherein the method further comprises identifying periods where the user is awake by using actigraphy data derived from the measured movements of the user.

61. The method of claim 55, wherein the method further comprises calculating an amount of time that has passed since the user last slept from the identified periods where the user is asleep and where the user is awake.

62. The method of claim 36, wherein the method further comprises:

detecting, with the motion sensor, changes in at least one of;

reaction time, range of motion, rotational velocity, rotational acceleration, rotational jerk, total time of motion, timing of motion, or other linear movements of each measured response of the user to each stimulus by comparing, with a processor coupled to the wearable apparatus, the base responsiveness profile to each responsiveness profile for each response; and

incorporating the detected changes in the at least one of reaction time, range of motion, rotational velocity, rotational acceleration, rotational jerk, total time of motion, timing of motion, or other linear movements into generating dynamic risk levels of the user falling asleep.

63. The method of claim 36, wherein the method further comprises:

measuring, with the motion sensor, relative movements of the user, and

adjusting the detected changes in the at least one sleep risk variable of the user with the measured relative movements of the user.

64. The method of claim 63, wherein the at least one sleep risk variable is a biomarker of the user.

65. The method of claim 64, wherein the biomarker of the user is skin temperature.

66. The method of claim 38, wherein the method further comprises:

identifying masking events that distort the user'"'"'s estimated personal circadian rhythm, and

adjusting the estimated personal circadian rhythm of the user based on the identified masking events.

67. The method of claim 66, wherein the masking events include positions of the user'"'"'s body and movements of the user.

68. The method of claim 36, wherein the method further comprises:

monitoring changes in the generated dynamic risk levels of the user over time;

monitoring changes in the at least one sleep risk variable over time;

calculating progress of the user in managing fatigue from the monitored changes in the dynamic risk levels and from the monitored changes in the at least one sleep risk variable; and

providing the user with daily and real-time fatigue data derived from the calculated progress of the user in managing fatigue.

69. The method of claim 68, wherein the at least one sleep risk variable is sleep drive of the user.

70. The method of claim 68, wherein the method further comprises providing a third party with the daily and the real-time fatigue data derived from the calculated progress of the user in managing fatigue.