METHODS AND APPARATUS FOR MONITORING ALTERNESS OF AN INDIVIDUAL UTILIZING A WEARABLE DEVICE AND PROVIDING NOTIFICATION

• US 20180342143A1
• Filed: 07/16/2018
• Published: 11/29/2018
• Est. Priority Date: 09/09/2014
• Status: Active Grant

• Current Assignee: Curgroup Incorporated
• Original Assignee: Torvec, Inc.
• Inventors: Kaplan, Richard A., Hemink, Douglas A., Kenyon, Matt
• Granted Patent: US 10,339,781 B2
• Time in Patent Office: Days
• 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 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. Motion of the individual is monitored to detect a predescribed motion in response to a stimulus to first determine a base responsiveness profile. Afterwards, a current responsiveness profile is determined based on a prescribed motion in response to a stimulus, and if the current responsiveness profile exceeds a predetermined threshold of the base responsiveness profile, a notification is issued to the individual and, optionally, another person such as an employer, teacher, or parent.

Claims

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

a wearable apparatus comprising;

a sensor configured to measure biomarkers of the user;

a communication module; and

a support configured to support the sensor; and

a remote device comprising;

memory;

a notification generator configured to generate a warning to the user via the communication module in response to receipt of a signal;

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

obtain, from the communication module, biomarker measurements from the sensor;

generate a personal circadian rhythm for the user, and a position of the user along that circadian rhythm, using the measured biomarkers;

identify a baseline risk level for the user by assessing the personal circadian rhythm of the user and the position of the user along that personal circadian rhythm;

determine a sleep drive of the user based upon continual or periodic sensor measurements, wherein sleep and wakefulness cycles of the user are derived from actigraphy data obtained from the sensor; and

send the signal to the notification generator when the baseline risk level is in a predetermined range.

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

generate a mask event of the personal circadian rhythm based upon changes in body position or activity of the user; and

remove the mask event from a personal circadian rhythm estimate by scaling data from the personal circadian rhythm to correct for body position as measured by the sensor and averaging personal circadian rhythm data in a testing period.

3. The system of claim 2, wherein the processor is further configured to:

scale the personal circadian rhythm data to generate corrected data for an activity of the user during the testing period as measured by the sensor; and

generate a more accurate estimate of the personal circadian rhythm based on the corrected data.

4. The system of claim 1, wherein the processor is further configured to generate the personal circadian rhythm, and the position of the user along that personal circadian rhythm, based upon a skin temperature of the user as measured by the sensor.

5. The system of claim 4, wherein the processor is further configured to generate the personal circadian rhythm by correlating a distal skin temperature of the user to a core body temperature of the user.

6. A method for monitoring fatigue and notifying a user, comprising:

affixing on the user an apparatus comprising a sensor and a communication module, wherein the sensor is in communication, via the communication module, with a notification generator and a processor configured to detect and monitor biomarkers of the user;

generating, by the processor, an estimated personal circadian rhythm for the user based upon measured biomarkers;

determining, by the processor, a dynamic risk level for the user based on the estimated personal circadian rhythm of the user, and a position of the user along that estimated personal circadian rhythm;

determining, by the processor, a sleep drive of the user based upon continual or periodic sensor measurements, wherein sleep and wakefulness cycles of the user are derived from actigraphy data obtained from the sensor; and

sending a signal to the notification generator when an identified risk level is in a predetermined range such that the notification generator outputs an alert.

7. The method of claim 6, further comprising:

generating a mask event of the estimated personal circadian rhythm based upon changes in body position or activity of the user; and

removing the mask event from the estimated personal circadian rhythm by scaling data from the circadian rhythm to correct for body position as measured by the sensor and averaging the circadian rhythm data in a testing period.

8. The method of claim 7, further comprising:

scaling the circadian rhythm data to generate corrected data for an activity of the user during the testing period as measured by the sensor; and

generating a more accurate estimate of the personal circadian rhythm based on the corrected data.

9. The method of claim 6, wherein one of the biomarkers is skin temperature.

10. The method of claim 6, wherein biomarker measurements relied upon to determine the dynamic risk level are made at the sensor.

11. The method of claim 10, wherein the sensor is located on a distal portion of the user.

12. A wearable device for monitoring fatigue comprising:

a sensor configured to measure biomarkers and actigraphy data of a user, wherein the sensor is communicatively coupled to;

a remote device comprising memory and a processor; and

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

a communication module configured to;

output, to the remote device, biomarker measurements from the sensor;

receive, from the remote device;

personal circadian rhythm for the user, and a position of the user along that circadian rhythm, using the measured biomarkers;

a baseline risk level for the user by assessing the personal circadian rhythm of the user and the position of the user along that personal circadian rhythm; and

a sleep drive of the user based upon continual or periodic sensor measurements, wherein sleep and wakefulness cycles of the user are derived from actigraphy data obtained from the sensor; and

receive, from the notification generator, the signal when the baseline risk level is in a predetermined range; and

a support configured to support the sensor and the communication module.

13. The wearable device of claim 12, wherein the communication module is further configured to:

generate a mask event of the personal circadian rhythm based upon changes in body position or activity of the user; and

remove the mask event from a personal circadian rhythm estimate by scaling data from the personal circadian rhythm to correct for body position as measured by the sensor and averaging personal circadian rhythm data in a testing period.

14. The wearable device of claim 13, wherein the communication module is further configured to:

scale the personal circadian rhythm data to generate corrected data for an activity of the user during the testing period as measured by the sensor; and

generate a more accurate estimate of the personal circadian rhythm based on the corrected data.

15. The system of claim 12, wherein the communication module is further configured to generate the personal circadian rhythm, and the position of the user along that personal circadian rhythm, based upon a skin temperature of the user as measured by the sensor.

16. The system of claim 15, wherein the communication module is further configured to generate the personal circadian rhythm by correlating a distal skin temperature of the user to a core body temperature of the user.

17. A method for monitoring fatigue, comprising:

affixing on a user a wearable device comprising a sensor and a communication module;

measuring, by the sensor, biomarkers and actigraphy data from the user;

outputting, by the communication module, the biomarkers and actigraphy data from the wearable device to a remote device;

receiving, at the sensor, personal circadian rhythm for the user, and a position of the user along that circadian rhythm, using the measured biomarkers;

receiving, at the communication module, a baseline risk level for the user by assessing the personal circadian rhythm of the user and the position of the user along that personal circadian rhythm; and

receiving, at the sensor, a sleep drive of the user based upon continual or periodic sensor measurements, wherein sleep and wakefulness cycles of the user are derived from actigraphy data obtained from the sensor; and

receiving at the communication module, from (i) the remote device or (ii) a third party device that is remote with respect to the wearable device and the remote device, a signal based upon an identified risk level being in a predetermined range such that a notification generator outputs an alert.

18. The method of claim 17, further comprising:

generating a mask event of the estimated personal circadian rhythm based upon changes in body position or activity of the user; and

removing the mask event from the estimated personal circadian rhythm by scaling data from the circadian rhythm to correct for body position as measured by the sensor and averaging the circadian rhythm data in a testing period.

19. The method of claim 18, further comprising:

scaling the circadian rhythm data to generate corrected data for an activity of the user during the testing period as measured by the sensor; and

generating a more accurate estimate of the personal circadian rhythm based on the corrected data.

20. The method of claim 17, wherein one of the biomarkers is skin temperature.

21. The method of claim 17, wherein biomarker measurements relied upon to determine the dynamic risk level are made at the sensor.

22. The method of claim 21, wherein the sensor is located on a distal portion of the user.

23. A computing device for monitoring fatigue, the computing device comprising:

memory;

a notification generator configured to generate a warning in response to receipt of a signal;

a communication module; and

a processor communicatively coupled to the notification generator and, via the communication module, to a user device worn by a user, the processor configured to;

obtain, by the communication module, biomarker measurements about the user from a sensor in the user device;

generate a personal circadian rhythm for the user, and a position of the user along that circadian rhythm, using the measured biomarkers;

identify a baseline risk level for the user by assessing the personal circadian rhythm of the user and the position of the user along that personal circadian rhythm;

determine a sleep drive of the user based upon continual or periodic sensor measurements, wherein sleep and wakefulness cycles of the user are derived from actigraphy data obtained from the sensor; and

output, by the communication module, the signal to the notification generator when the baseline risk level is in a predetermined range.

24. The computing device of claim 23, wherein the processor is further configured to:

generate a mask event of the personal circadian rhythm based upon changes in body position or activity of the user; and

remove the mask event from a personal circadian rhythm estimate by scaling data from the personal circadian rhythm to correct for body position as measured by the sensor and averaging personal circadian rhythm data in a testing period.

25. The computing device of claim 24, wherein the processor is further configured to:

scale the personal circadian rhythm data to generate corrected data for an activity of the user during the testing period as measured by the sensor; and

generate a more accurate estimate of the personal circadian rhythm based on the corrected data.

26. The system of claim 23, wherein the processor is further configured to generate the personal circadian rhythm, and the position of the user along that personal circadian rhythm, based upon a skin temperature of the user as measured by the sensor.

27. The system of claim 26, wherein the processor is further configured to generate the personal circadian rhythm by correlating a distal skin temperature of the user to a core body temperature of the user.

28. A method for monitoring fatigue, comprising:

receiving at a communication module in a first device, from a wearable device worn by a user and communicatively coupled to the first device, biomarkers and actigraphy data from the user;

generating, by a processor in the first device, an estimated personal circadian rhythm for the user based upon measured biomarkers;

determining, by the processor, a dynamic risk level for the user based on the estimated personal circadian rhythm of the user, and a position of the user along that estimated personal circadian rhythm;

determining a sleep drive of the user based upon continual or periodic measurements from a sensor, wherein sleep and wakefulness cycles of the user are derived from actigraphy data obtained from the sensor; and

outputting, from the communication module to (i) the user-worn device or (ii) a third party device that is remote with respect to both the first device and the wearable device, a signal when an identified risk level is in a predetermined range.

29. The method of claim 28, further comprising:

generating a mask event of the estimated personal circadian rhythm based upon changes in body position or activity of the user; and

removing the mask event from the estimated personal circadian rhythm by scaling data from the circadian rhythm to correct for body position as measured by the sensor and averaging the circadian rhythm data in a testing period.

30. The method of claim 29, further comprising:

scaling the circadian rhythm data to generate corrected data for an activity of the user during the testing period as measured by the sensor; and

generating a more accurate estimate of the personal circadian rhythm based on the corrected data.

31. The method of claim 28, wherein one of the biomarkers is skin temperature.

32. The method of claim 28, wherein biomarker measurements relied upon to determine the dynamic risk level are made at the sensor.

33. The method of claim 32, wherein the sensor is located on a distal portion of the user.