Systems, methods and devices for providing an exertion recommendation based on performance capacity

• US 10,129,628 B2
• Filed: 02/01/2016
• Issued: 11/13/2018
• Est. Priority Date: 02/01/2016
• Status: Active Grant

• Current Assignee: Logitech Europe S A (Logitech International SA)
• Original Assignee: Logitech Europe S A (Logitech International SA)
• Inventors: Wisbey, Ben, Armstrong, Judd, Diesterbeck, Hagen, Shepherd, David
• Primary Examiner(s): McClellan, James S
• Application Number:
  US15/012,776
• Publication Number:
  US 20170216672A1
• Time in Patent Office: 1,016 Days
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/6803 Head-worn items, e.g. helmets, masks, headphones or goggles
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6815 Ear
• CPC Class Codes: G09B 19/003 Repetitive work cycles; Sequence of movements
• CPC Class Codes: H04R 1/1016 Earpieces of the intra-aural type
• CPC Class Codes: H04R 1/1041 Mechanical or electronic switches, or control elements switches in general H01H
• CPC Class Codes: H04R 2420/09 Applications of special connectors, e.g. USB, XLR, in loudspeakers, microphones or headphones
• CPC Class Codes: H04R 2430/01 Aspects of volume control, not necessarily automatic, in sound systems

Abstract

Systems, methods, and devices are provided for determining an exertion recommendation for an anticipated exercise session. One such system includes a wearable device comprising a biosensor that monitors biometrics (e.g. heart rate); a motion sensor that monitors activity; a processor operatively coupled to the biosensor, the processor configured to process electronic signals periodically generated by the biosensor and the motion sensor; and a non-transitory computer-readable medium operatively coupled to the processor and storing instructions that, when executed, cause the processor to execute specific functions. In particular, the instructions are executed to cause the processor to generate biometric data from the biometrics (e.g. heart rate information in particular). Further, the instructions are executed to generate an exertion recommendation based on an exertion model created from and representing a relationship between prior exertion measures and prior response profile measures.

Claims

1. A system for providing an exertion recommendation, the system comprising:

a wearable device, comprising;

a heart rate sensor that is configured to monitor a heart rate of a user wearing the wearable device;

a motion sensor that is configured to monitor activity of the user wearing the wearable device;

a processor operatively coupled to the heart rate sensor and the motion sensor, the processor configured to process electronic signals generated by the heart rate sensor and the motion sensor and generate one or more of heart rate data and activity data therefrom; and

a non-transitory computer-readable medium operatively coupled to the processor and storing instructions that, when executed, cause the processor to;

generate an exertion load recommendation for an exercise session based on a targeted performance capacity, wherein the exertion load recommendation provides the user with a plan to attain the targeted performance capacity that is based on the user'"'"'s capacity to perform exercise after completing the exercise session, wherein

the exertion load recommendation is generated from an exertion model that associates the user'"'"'s exertion load to the user'"'"'s performance capacity based on heart rate data and activity data collected from the wearable device during a plurality of previous exercise sessions when the wearable device was worn by the user, and

the exertion model is based on data from the heart rate monitored by the heart rate sensor and the activity monitored by the motion sensor;

determine the exertion load during the exercise session based on accumulating exercise intensity of the user during the exercise session, wherein the exercise intensity of the user is based on the heart rate data; and

provide the determined exertion load to the user during the exercise session.

2. The system of claim 1, wherein the wearable device comprises one or more of an earphone and a wristband.

3. The system of claim 1, wherein the heart rate sensor comprises one or more of a finger heart rate sensor, a wrist heart rate sensor, and an optical heartrate sensor.

4. The system of claim 1, wherein the heart rate data includes one or more of a heart rate and a heart rate variability.

5. The system of claim 1, wherein the exertion recommendation is further based on the amount of time the exercise session is anticipated to take.

6. The system of claim 1, further comprising:

a first wireless transceiver embedded in the wearable device;

a computing device; and

a second wireless transceiver embedded in the computing device operatively coupled to the wearable device, wherein the first and second wireless transceivers are configured to receive and send one or more of the heart rate data and the activity data.

7. The system of claim 1, wherein the exertion model associates the user'"'"'s exertion load to changes in the user'"'"'s performance capacity over a duration of each exercise session.

8. A system for providing an exertion recommendation, the system comprising:

an electronic clock configured to monitor time;

a wearable device, comprising;

a heart rate sensor that is configured to monitor a heart rate of a user wearing the wearable device;

a motion sensor that is configured to monitor activity of the user wearing the wearable device;

a processor operatively coupled to the heart rate sensor and the motion sensor, the processor configured to process electronic signals generated by the heart rate sensor and the motion sensor and generate one or more of heart rate data and activity data therefrom; and

a non-transitory computer-readable medium operatively coupled to the processor and storing instructions that, when executed, cause the processor to;

generate an exertion load recommendation for a future exercise session based on a targeted performance capacity that represents the user'"'"'s capacity to perform exercise after completing the future exercise session, wherein the exertion load recommendation provides the user with a plan to attain the targeted performance capacity,

wherein the exertion load recommendation for the future exercise session is generated from an exertion model that associates the user'"'"'s exertion load to the user'"'"'s performance capacity based on heart rate data and activity data collected from the wearable device during a plurality of previous exercise sessions when the wearable device was worn by the user, and

the exertion model is based on data from the heart rate monitored by the heart rate sensor and the activity monitored by the motion sensor;

determine the exertion load during the future exercise session based on accumulating exercise intensity of the user during the future exercise session, wherein the exercise intensity of the user is based on the heart rate data; and

provide the determined exertion load to the user during the future exercise session.

9. The system of claim 8, wherein the wearable device comprises one or more of an earphone and a wristband.

10. The system of claim 8, wherein the heart rate sensor comprises one or more of a finger heart rate sensor, a wrist heart rate sensor, and an optical heartrate sensor.

11. The system of claim 8, wherein the heart rate data includes one or more of a heart rate and a heart rate variability.

12. The system of claim 8, wherein:

the exertion model is further based on the amount of time taken to complete a previous exercise session as monitored by the electronic clock; and

the exertion recommendation is further based on the amount of time the exercise session is anticipated to take.

13. The system of claim 8, wherein the exertion model associates the user'"'"'s exertion load to changes in the user'"'"'s performance capacity over a duration of each exercise session.

14. The system of claim 8, further comprising:

a first wireless transceiver embedded in the wearable device;

a computing device; and

a second wireless transceiver embedded in the computing device operatively coupled to the wearable device, wherein, the first and second wireless transceivers are configured to receive and send one or more of the heart rate data and the activity data.

15. A computer-implemented method for determining an exertion recommendation for an anticipated exercise session, the method comprising:

measuring a heart rate of a user during a plurality of exercise sessions using a heart rate sensor embedded in a wearable device;

measuring activity of a user during the plurality of exercise sessions using a motion sensor embedded in the wearable device;

generating heart rate data from the measured heart rate;

generating activity data from the measured activity;

generating a plurality of exercise intensity values during each exercise session based on the heart rate measured by the heart rate sensor and the activity measured by the motion sensor during that exercise session;

generating an exertion load value for each exercise session based on accumulating the plurality of exercise intensity values generated during that exercise session;

determining a change in performance capacity of the user that occurs as a result of the user performing each exercise session, wherein the performance capacity is the user'"'"'s capacity to perform exercise;

generating an exertion model that associates the user'"'"'s exertion load to the user'"'"'s change in performance capacity based on the heart rate data and the activity data collected from the wearable device during the plurality of exercise sessions when the wearable device was worn by the user; and

generating an exertion load recommendation for a future exercise session based on a targeted performance capacity that represents the user'"'"'s capacity to perform exercise after completing the future exercise session, wherein the exertion load recommendation provides the user with a plan to attain the targeted performance capacity,

wherein the exertion load recommendation for the future exercise session is generated from the exertion model.

16. The computer-implemented method of claim 15, wherein generating the exertion load value for a given exercise session comprises:

weighting each of a plurality of exercise intensity values based on each exercise intensity value'"'"'s proximity in time to the most recent exercise intensity value generated during that exercise session;

aggregating the weighted plurality of exercise intensity values over a measuring period; and

generating the exertion load value for that exercise session based on the aggregation of the weighted plurality of exercise intensity values for the measuring period.

17. The computer-implemented method of claim 15, wherein generating the exertion model comprises:

calculating a current HRV value from the heart rate data;

calculating, based on a set of HRV values previously calculated using the heart rate data, an average HRV value and an HRV variation;

calculating an HRV score based on a difference between the average HRV value and the current HRV value, the difference being scaled by the HRV variation; and

creating the exertion model based on one or more a heart rate variability (HRV) score based on the heart rate data, a fatigue score based on the activity data, a predicted HRV score based on the heart rate data and the activity data, and a predicted fatigue score based on one or more of the heart rate data and the activity data.

18. The computer-implemented method of claim 15, wherein the wearable device comprises one or more of an earphone and a wristband.

19. The computer-implemented method of claim 15, wherein the heart rate sensor comprises one or more of a finger heart rate sensor, a wrist heart rate sensor, and an optical heartrate sensor.

20. The computer-implemented method of claim 15, wherein the heart rate data includes one or more of a heart rate and a heart rate variability.

21. The computer-implemented method of claim 15, wherein:

the exertion model is further based on an amount of time taken to complete the plurality of the exercise sessions; and

the exertion load recommendation is further based on an amount of time the future exercise session is anticipated to take.

22. The computer-implemented method of claim 15, further comprising:

receiving one or more of heart rate data and activity data at a computing device having a wireless receiver.

23. The computer-implemented method of claim 15, further comprising

determining the exertion load during the future exercise session based on accumulating exercise intensity of the user during the future exercise session, wherein the exercise intensity of the user is based on the heart rate data;

and

providing the determined exertion load to the user during the future exercise session.