Personal fatigue risk management system and method

• US 9,241,658 B2
• Filed: 09/19/2012
• Issued: 01/26/2016
• 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
• Primary Examiner(s): Dougherty, Sean
• Assistant Examiner(s): CERIONI, DANIEL LEE
• Application Number:
  US13/622,720
• Publication Number:
  US 20140081179A1
• Time in Patent Office: 1,224 Days
• Field of Search: 600/587, 600/595, 205/87, 205/95
• US Class Current: 1/1
• 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:

a) receiving a signal from the accelerometer indicating an acceleration of the smartwatch and calculating a displacement or change in position of the smartwatch based on this acceleration, the extent of displacement or change in position represented by a movement value;

b) storing the movement value and an indicia of the time at which the signal was received in a time-stamped bin, the time-stamped bin stored in memory locations within the associated memory;

c) repeating steps a) and b) to obtain a plurality of stored movement values for a predetermined time interval;

d) analyzing the plurality of stored movement values, using a state algorithm, to determine a sleep state value, an off-wrist state value or a wake state value for a person wearing the smartwatch for the predetermined time interval;

e) repeating steps a) through d) to obtain a plurality of sleep state values, wake state values, and/or off-wrist state values;

f) receiving a task input or a location input indicative of an activity being performed by a wearer of the smartwatch;

g) analyzing a plurality of the sleep state values and wake state values, using a fatigue risk algorithm, to determine fatigue risk values, the fatigue risk values determined using at least the plurality of sleep state values, wake state values and off-wrist state values, and the task input or the location input indicative of the activity being performed by the wearer of the smartwatch;

h) determining a probable relative risk of errors and incidents indicative of at least a probability of having an error, incident, or accident that is caused at least in part by fatigue as compared to an average risk of errors and incidents for the wearer of the smartwatch, using at least a comparison of the fatigue risk values with a reference index based at least on an aggregate of fatigue-related risk data collected for a plurality of other users and the activity being performed by the wearer of the smartwatch; and

i) electronically causing a warning to be issued, via electronic components of the smartwatch, when the probable relative risk of errors and incidents as compared to the average risk of errors and incidents breaches one or more predetermined thresholds for the activity being performed by the wearer of the smartwatch,

wherein the warning includes a display indicating one or more corrective actions, the one or more corrective actions selected based on the activity being performed by the wearer of the smartwatch.

2. The method of claim 1, wherein analyzing the plurality of stored movement values to determine the sleep state value, the off-wrist state value and the wake state value further comprises determining a cumulative sum of the stored movement values of the smartwatch during a predetermined time interval and incrementally updating the sleep state value, the off-wrist state value or the wake state value using look-back weighted averaging over the most recent stored movement value and a predetermined number of previous stored movement values.

3. The method of claim 1, further comprising:

recording a time of occurrence of a start or end of activity 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 parameters and work-rest parameters using look-back weighted averaging of a predetermined number of the stored sleep state values, wake state values, and off-wrist state values;

wherein the duty-rest parameters 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 the start or end of activity 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 the start of end of activity event(s).

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

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

7. The method of claim 3, wherein the smartwatch receives a task 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 and utilized as part of the location input.

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 location data;

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

calculating compliance, of the wearer, with duty-rest regulations, based on the linked location 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.