Exercise behavior prediction

• US 9,691,023 B2
• Filed: 11/30/2014
• Issued: 06/27/2017
• Est. Priority Date: 11/30/2014
• Status: Active Grant

• Current Assignee: CarePredict, Inc.
• Original Assignee: WiseWear Corporation
• Inventors: Beens, Jason A., Barnes, Ronald A., Elam, Jr., David P., Ibey, Bennett L., Wilmink, Gerald J.
• Primary Examiner(s): Hill, Stanley K
• Assistant Examiner(s): OLUDE AFOLABI, OLATOYOSI
• Application Number:
  US14/556,191
• Publication Number:
  US 20160151668A1
• Time in Patent Office: 940 Days
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 10/1095 Meeting or appointment
• CPC Class Codes: G09B 7/02 of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising

Abstract

Methods, apparatuses, and computer readable mediums for exercise behavior prediction are provided. In a particular embodiment, the prediction evaluation controller is configured to generate an exercise activity pattern based on correlations between scheduling of a user'"'"'s historical non-exercise events and the user'"'"'s historical exercise events. In the particular embodiment, the prediction evaluation controller is also configured to generate, based on the generated exercise activity pattern, by the prediction evaluation controller, a future exercise event to correspond with a future non-exercise event scheduled on the user'"'"'s calendar. In the particular embodiment, the prediction evaluation controller is also configured to provide an indication of the generated future exercise event.

Claims

1. A method of exercise behavior prediction, the method comprising:

generating, by a prediction evaluation controller, an exercise activity pattern based on correlations between scheduling of a user'"'"'s historical non-exercise events and the user'"'"'s historical exercise events, wherein the correlation is a repeated scheduling of exercise events and non-exercise events, and wherein the user'"'"'s historical non-exercise events and the user'"'"'s historical exercise events are calendar events on the user'"'"'s calendar indicating that the user performed a non-exercise activity and an exercise at a particular time before the user'"'"'s current time;

generating, based on the generated exercise activity pattern, by the prediction evaluation controller, a future exercise event to correspond with a future non-exercise event scheduled on the user'"'"'s calendar; and

providing, by the prediction evaluation controller, an indication of the generated future exercise event, wherein providing the indication of the generated future exercise event includes sending to the user, a new event request to schedule the generated future exercise event on the user'"'"'s calendar.

2. The method of claim 1 wherein generating a future exercise event includes selecting from a plurality of exercise activities, a particular exercise activity for the user to perform during the generated future exercise event.

3. The method of claim 2 wherein selecting the particular exercise activity is based on an evaluation of a user'"'"'s performance of one or more exercise activities during the user'"'"'s historic exercise events.

4. The method of claim 2 wherein selecting the particular exercise activity is based on historic physiological data indicating at least one vital sign of the user during the user'"'"'s performance of the one or more exercise activities during the user'"'"'s historic exercise events.

5. The method of claim 2 wherein selecting the particular exercise activity is based on historic environmental conditions during the user'"'"'s performance of the one or more exercise activities during the user'"'"'s historic exercise events.

6. The method of claim 2 wherein selecting the particular exercise activity is based on current physiological data indicating at least one vital sign of the user.

7. The method of claim 2 wherein selecting the particular exercise activity is based on a forecast of environmental conditions anticipated during the generated future exercise event.

8. The method of claim 2 wherein selecting the particular exercise activity is based on a fitness goal of the user.

9. The method of claim 1 wherein providing the indication of the generated future exercise event further includes automatically scheduling the generated future exercise event on the user'"'"'s calendar.

10. The method of claim 1 further comprising:

generating, based on physiological data indicating at least one vital sign of the user, by the prediction evaluation controller, a physiological improvement recommendation indicating an activity to improve a user'"'"'s physiological condition for performance of the generated future exercise event; and

providing, by the prediction evaluation controller, the generated physiological improvement recommendation.

11. The method of claim 10 wherein generating the physiological improvement recommendation is further based on a forecast of environmental conditions anticipated during the generated future exercise event.

12. The method of claim 10 wherein providing the generated physiological improvement recommendation includes sending to the user, a new event request to schedule the activity indicated in the identified physiological improvement recommendation.

13. The method of claim 10 wherein providing the generated physiological improvement recommendation includes automatically scheduling the activity indicated in the identified physiological improvement recommendation.

14. The method of claim 1 wherein generating an exercise activity pattern based on correlations between scheduling of a user'"'"'s historical non-exercise events and the user'"'"'s historical exercise events includes identifying a pattern in the duration between the user'"'"'s non-exercise events and the user'"'"'s correlated exercise events.

15. The method of claim 1 wherein generating an exercise activity pattern based on correlations between scheduling of a historical user'"'"'s non-exercise events and the user'"'"'s historical exercise events includes identifying a pattern in type of the user'"'"'s non-exercise events and the user'"'"'s correlated exercise events.

16. An apparatus for exercise behavior prediction, the apparatus comprising a computer processor and computer memory operatively coupled to the computer processor, the computer memory having disposed within it computer program instructions that, when executed by the computer processor, cause the apparatus to carry out the steps of:

generating, by a prediction evaluation controller, an exercise activity pattern based on correlations between scheduling of a user'"'"'s historical non-exercise events and the user'"'"'s historical exercise events, wherein the correlation is a repeated scheduling of exercise events and non-exercise events, and wherein the user'"'"'s historical non-exercise events and the user'"'"'s historical exercise events are calendar events on the user'"'"'s calendar indicating that the user performed a non-exercise activity and an exercise at a particular time before the user'"'"'s current time;

generating, based on the generated exercise activity pattern, by the prediction evaluation controller, a future exercise event to correspond with a future non-exercise events scheduled on the user'"'"'s calendar; and

providing, by the prediction evaluation controller, an indication of the generated future exercise event, wherein providing the indication of the generated future exercise event includes sending to the user, a new event request to schedule the generated future exercise event on the user'"'"'s calendar.

17. The apparatus of claim 16 further comprising computer program instructions that, when executed by the computer processor, cause the apparatus to carry out the steps of:

generating, based on physiological data indicating at least one vital sign of the user, by the prediction evaluation controller, a physiological improvement recommendation indicating an activity to improve a user'"'"'s physiological condition for performance of the generated future exercise event; and

providing, by the prediction evaluation controller, the generated physiological improvement recommendation.

18. A non-transitory computer readable storage medium storing instructions executable by a computer for exercise behavior prediction, the instructions comprising:

instructions that are executable by the computer to generate, by a prediction evaluation controller, an exercise activity pattern based on correlations between scheduling of a user'"'"'s historical non-exercise events and the user'"'"'s historical exercise events, wherein the correlation is a repeated scheduling of exercise events and non-exercise events, and wherein the user'"'"'s historical non-exercise events and the user'"'"'s historical exercise events are calendar events on the user'"'"'s calendar indicating that the user performed a non-exercise activity and an exercise at a particular time before the user'"'"'s current time;

instructions that are executable by the computer to generate, based on the generated exercise activity pattern, by the prediction evaluation controller, a future exercise event to correspond with a future non-exercise events scheduled on the user'"'"'s calendar; and

instructions that are executable by the computer to provide, by the prediction evaluation controller, an indication of the generated future exercise event, wherein providing the indication of the generated future exercise event includes sending to the user, a new event request to schedule the generated future exercise event on the user'"'"'s calendar.

19. The computer readable storage medium of claim 18 wherein the instructions are executable by a processor integrated into a device selected from the group consisting of a navigation device, a communications device, a personal digital assistant (PDA), a fixed location data unit, and a computer.