Personal Fatigue Risk Management System And Method

• US 20140081179A1
• Filed: 09/19/2012
• Published: 03/20/2014
• Est. Priority Date: 09/19/2012
• Status: Active Grant

• Current Assignee: Martin Christopher Moore-Ede
• Original Assignee: Martin Christopher Moore-Ede
• Inventors: Moore-Ede, Martin Christopher
• Granted Patent: US 9,241,658 B2
• Time in Patent Office: Days
• US Class Current: 600/595
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• 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 5/681 Wristwatch-type devices
• 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: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A system and method for personal fatigue risk management, performed using a smartwatch including a digital processor, associated memory, and an accelerometer for measuring acceleration in X,Y, and Z axes. A signal is received from the accelerometer indicating a movement of the smartwatch, and the value of the signal is stored in association with indicia of the time at which the signal was received in a time-stamped bin. The stored movement values are analyzed using a state algorithm to determine sleep, wake, and off-wrist state values. Fatigue risk values are determined from the sleep and wake state values, using a state algorithm. The probable relative risk of errors and incidents as compared to average risk, for the wearer of the watch, are then calculated. A warning is issued when the fatigue risk data indicates that one of the thresholds has been exceeded.

Claims

1. A method for personal fatigue risk management, performed using a smartwatch including a digital processor, associated memory, and an accelerometer for measuring acceleration in X,Y, and Z axes, the method comprising:

receiving a signal from the accelerometer indicating a movement of the smartwatch;

storing the value of the signal in association with indicia of the time at which the signal was received in a time-stamped bin comprising a segment of said memory;

storing, in a respective time-stamped bin, a cumulative sum of each of the movements over a predetermined time interval;

analyzing a plurality of the stored movement values, using a state algorithm, to determine sleep and wake state values for a person wearing the watch, and off-wrist state values wherein each said sleep and wake and off-wrist state value is stored in a respective said time-stamped bin;

repeating the steps of receiving, storing, and analyzing the stored accelerometer values to incrementally update the sleep, wake, and off-wrist state values;

analyzing a plurality of the stored sleep and wake state values, using a fatigue risk algorithm, to determine fatigue risk values;

calculating a probable relative risk of errors and incidents as compared to average risk for the wearer of the watch, using the fatigue risk values;

indentifying a fatigue risk increase based on the calculated fatigue risk data and predetermined thresholds;

wherein a fatigue risk algorithm is used to perform the steps of calculating and indentifying; and

issuing a warning, via the smartwatch, when the fatigue risk data indicates that one of the thresholds has been exceeded.

2. The method of claim 1, wherein each said time-stamped bin containing the cumulative sum of movements of the smartwatch during a predetermined time interval is incrementally updated using look-back weighted averaging over the most recent stored value and a predetermined number of previous stored values.

3. The method of claim 1, wherein the smartwatch performs functions including:

recording a time of occurrence of an event selected from the set of events consisting of Beginning of Duty, End of Duty, Begin Commute, End Commute, Begin Deadhead, End Deadhead, Begin Safety-critical Task, End Safety-critical Task, Go to Bed, and Arise from Bed; and

computing and incrementally updating duty-rest and work-rest parameters using look-back weighted averaging of a predetermined number of the stored sleep, wake, and off-wrist states;

wherein the duty-rest and work-rest parameters include time of day of last End of Duty event, duration of last off-duty episode, and duration of current on-duty episode since the last recorded Off-Duty to On-Duty event transition.

4. The method of claim 3, wherein each said event is wirelessly transmitted by the smartwatch to an external computing device and the incrementally updating is performed by the external device.

5. The method of claim 3, including receiving, from a user pressing a button on the smartwatch, a task signal input indicative of one of the events.

6. The method of claim 3, including receiving, via a wireless signal, a task signal input indicative of one of the events;

wherein the smartwatch includes a transceiver for communication with a wireless device.

7. The method of claim 3, wherein the smartwatch receives the task signal input wirelessly from a device selected from the set of devices consisting of a workplace work time entry system, a vehicle engine control module, and an aircraft cockpit signal.

8. The method of claim 1, wherein the smartwatch issues the warning via a display on the smartwatch.

9. The method of claim 1, wherein an indication of the warning is received from a device wirelessly connected to the smartwatch.

10. The method of claim 1, wherein the fatigue risk algorithm is executed on a processing entity, external to the smartwatch, in wireless communication with the smartwatch.

11. The method of claim 1, wherein:

the smartwatch includes a Global Positioning System receiver to acquire location data indicative of the location of the smartwatch; and

the location data is stored in association with corresponding time data.

12. The method of claim 11, wherein the location data is stored as a record of duty-rest times.

13. The method of claim 11, wherein the location data is stored as a record of time and attendance data.

14. The method of claim 11, further including:

linking the location data to information including Beginning of Duty and End of Duty events;

storing the linked data;

calculating compliance, of the wearer, with duty-rest regulations, based on the linked data; and

displaying a warning when a lack of compliance with the regulations is indicated by the calculated compliance.

15. The method of claim 14, wherein a warning is displayed indicating a predicted time until a lack of compliance event will occur.

16. The method of claim 1, further including:

acquiring location data indicative of the location of the smartwatch from one of a Global Positioning System, a workplace time clock, and a vehicle engine control module;

storing the location data in association with corresponding time data;

wherein the location data is stored as a record of one of duty-rest times and time and attendance data;

linking the location data to information including Begin Duty, and End Duty events;

storing the linked data;

calculating compliance, of the wearer, with duty-rest regulations, based on the linked data; and

displaying a warning when a lack of compliance with the regulations is indicated by the calculated compliance.

17. The method of claim 16, wherein a warning is displayed indicating a predicted time until a lack of compliance event will occur.

18. The method of claim 1, wherein the accelerometer measures only values determined from two of the X, Y or Z coordinates.

19. A method for personal fatigue risk management, performed using a smartwatch including a digital processor, associated memory, and an accelerometer for measuring acceleration in X,Y, and Z axes, the method comprising:

receiving a signal from the accelerometer indicating a movement of the smartwatch;

storing the value of the signal in association with indicia of the time at which the signal was received in a time-stamped bin comprising a segment of said memory;

storing, in a respective time-stamped bin, a cumulative sum of each of the movements over a predetermined time interval;

analyzing a plurality of the stored movement values, using a state algorithm, to determine sleep, wake, and off-wrist state values wherein each said sleep and wake and off-wrist state value is stored in a respective said time-stamped bin;

acquiring location data indicative of the location of the smartwatch from one of a workplace time clock. a vehicle engine control module, and aircraft cockpit instrumentation;

storing, in association with corresponding time data, the location data as a record of one of duty-rest times and time and attendance data;

recording a time of occurrence of events including Beginning of Duty and End of Duty;

linking the location data to information including the Beginning of Duty and End of Duty events;

calculating compliance, of the wearer, with duty-rest regulations, based on the linked data; and

displaying a warning when a lack of compliance with the regulations is indicated by the calculated compliance.

20. The method of claim 19, wherein a warning is displayed indicating a predicted time until a lack of compliance event will occur.

21. A system for personal fatigue risk management comprising:

a smartwatch including a digital processor having associated memory and an accelerometer for measuring acceleration in X,Y, and Z axes;

wherein the processor is programmed to perform tasks including;

receiving a signal from the accelerometer indicating a movement of the smartwatch;

storing the value of the signal in association with indicia of the time at which the signal was received in a time-stamped bin comprising a segment of said memory;

storing, in a respective time-stamped bin, a cumulative sum of each of the movements over a predetermined time interval;

analyzing a plurality of the stored movement values, using a state algorithm, to determine sleep, wake, and off-wrist state values wherein each said sleep, wake, and off-wrist state value is stored in a respective said time-stamped bin;

repeating the steps of receiving, storing, and analyzing to incrementally update the sleep, wake, and off-wrist state values;

analyzing a plurality of the stored sleep and wake state values, using a state algorithm, to determine fatigue risk values;

storing the fatigue risk values;

calculating the probable relative risk of errors and incidents as compared to average risk, for the wearer of the watch, using the fatigue risk values;

repeating the steps of analyzing and storing, to incrementally update the fatigue risk and error and incident values;

issuing a warning, via the smartwatch, when the fatigue risk data indicates that one of the thresholds has been exceeded.

22. The method of claim 21, wherein the smartwatch performs functions including:

recording a time of occurrence of an event selected from the set of events consisting of Beginning of Duty, End of Duty, Begin Commute, End Commute, Begin Deadhead, End Deadhead, Begin Safety-critical Task, End Safety-critical Task, Go to Bed, and Arise from Bed; and

computing and incrementally updating duty-rest and work-rest parameters using look-back weighted averaging of a predetermined number of the stored sleep, wake, and off-wrist states;

wherein the duty-rest and work-rest parameters include time of day of last End of Duty event, duration of last off-duty episode, and duration of current on-duty episode since the last recorded Off-Duty to On-Duty event transition.

23. The method of claim 21, wherein each said event is wirelessly transmitted by the smartwatch to an external computing device and the incrementally updating is performed by the external device.

24. The method of claim 23, including receiving, from a user pressing a button on the smartwatch, a task signal input indicative of one of the events.

25. The method of claim 23, including receiving, via a wireless signal, a task signal input indicative of one of the events;

wherein the smartwatch includes a transceiver for communication with a wireless device.

26. The method of claim 23, wherein the smartwatch receives the task signal input wirelessly from a device selected from the set of devices consisting of a workplace work time entry system, a vehicle engine control module, and an aircraft cockpit signal.

27. The method of claim 21, wherein:

the smartwatch includes a Global Positioning System receiver to acquire location data indicative of the location of the smartwatch; and

the location data is stored in association with corresponding time data.

28. The method of claim 27, wherein the location data is stored as a record of duty-rest times.

29. The method of claim 27, wherein the location data is stored as a record of time and attendance data.

30. The method of claim 27, further including:

linking the location data to information including Beginning of Duty and End of Duty events;

storing the linked data;

calculating compliance, of the wearer, with duty-rest regulations, based on the linked data; and

displaying a warning when a lack of compliance with the regulations is indicated by the calculated compliance.