Automated prescription of activity based on physical activity data

• US 10,744,373 B2
• Filed: 08/30/2019
• Issued: 08/18/2020
• Est. Priority Date: 11/08/2013
• Status: Active Grant

• Current Assignee: Pear Labs LLC
• Original Assignee: Performance Lab Technologies Ltd (Pear Sports, LLC)
• Inventors: Ackland, Jonathan Edward Bell
• Primary Examiner(s): D'Agostino, Paul A
• Application Number:
  US16/556,892
• Publication Number:
  US 20190388733A1
• Time in Patent Office: 354 Days
• Field of Search: None
• CPC Class Codes: A61B 2503/10 Athletes
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• 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 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/1114 Tracking parts of the body
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/14552 Details of sensors specially adapted therefor
• CPC Class Codes: A61B 5/4836 Diagnosis combined with treatment in closed-loop systems or methods A61B5/0036 takes precedence
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A63B 2024/0068 Comparison to target or threshold, previous performance or not real time comparison to other individuals
• CPC Class Codes: A63B 2220/17 Counting, e.g. counting periodical movements, revolutions or cycles, or including further data processing to determine distances or speed
• CPC Class Codes: A63B 2220/18 Inclination, slope or curvature
• CPC Class Codes: A63B 2220/22 Stride length
• CPC Class Codes: A63B 2220/72 Temperature
• CPC Class Codes: A63B 2220/73 Altitude
• CPC Class Codes: A63B 2220/75 Humidity
• CPC Class Codes: A63B 2220/76 Wind conditions
• CPC Class Codes: A63B 24/0062 : Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
• CPC Class Codes: A63B 24/0075 : Means for generating exercise programs or schemes, e.g. computerized virtual trainer, e.g. using expert databases
• CPC Class Codes: G06F 2218/12 : Classification; Matching
• CPC Class Codes: G06V 40/23 : Recognition of whole body movements, e.g. for sport training
• CPC Class Codes: G16H 20/30 : relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: Y02A 90/10 : Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The technology disclosed here involves analyzing an activity session. An example method involves receiving activity data indicative of an activity of a user, the activity data comprising a plurality of measurements associated with multiple parameters monitored during an activity session; retrieving a set of criteria corresponding to an activity type, wherein the set comprises a criterion related to a biomedical parameter and a criterion related to a resistance parameter; classifying, based on the set of criteria, the received activity data into a plurality of segments, wherein a first segment comprises a portion of the activity data corresponding to the activity type; accessing historical data associated with the user, the historical data comprising a second segment of historical data corresponding to the activity type; comparing the first segment comprising the portion of the received activity data and the second segment comprising historical data; and generating output based on the comparing.

Claims

1. A method comprising:

receiving, by a processor, activity data of one or more sensors that is indicative of an activity of a user, the activity data comprising a plurality of sensor measurements associated with multiple parameters monitored during an activity session;

retrieving a set of criteria that comprises thresholds to detect an activity type, wherein the set comprises a criterion related to a biomedical parameter and a criterion related to a resistance parameter;

classifying, by the processor based on the set of criteria, the received activity data of the one or more sensors into one or more segments, wherein the one or more segments comprise a first segment comprising a portion of the activity data corresponding to the activity type;

accessing historical data associated with the user, the historical data comprising a second segment of historical data corresponding to the activity type;

comparing the first segment comprising the portion of the received activity data and the second segment comprising historical data; and

generating output based on the comparing.

2. The method of claim 1 further comprising determining a performance value for the activity type based on the comparing of the first segment and the second segment.

3. The method of claim 2 wherein the output comprises an alert that is provided to the user during the activity session, wherein the activity session corresponds to a planned workout.

4. The method of claim 3 wherein the alert comprises one or more of a performance measure based on the performance value for the activity type, an instruction to modify the activity of the user during the activity session, or a modification to a training plan for the planned workout.

5. The method of claim 1 wherein the biomedical parameter comprises at least one of electrocardiograph (ECG), blood pressure (BP), body temperature, oxygen saturation, respiration rate, energy expenditure, or oxygen uptake.

6. The method of claim 1 wherein the resistance parameter comprises one of altitude, slope, gradient, stride rate, stroke rate, step rate, cadence, or distance per limb turnover.

7. The method of claim 1 wherein the activity session comprises activities associated with a plurality of different activity types, and further comprising detecting the plurality of activity types of the activity session by comparing the received activity data with a plurality of sets of criteria, wherein each of the plurality of sets corresponds to a multi-parameter zone for an activity type.

8. The method of claim 1 wherein the classifying comprises analyzing one or more measurements associated to the resistance parameter to determine an increase in altitude, a decrease in altitude, or a constant altitude.

9. The method of claim 1 wherein one of the multiple parameters comprises an effort parameter monitored during the activity session, wherein the effort parameter comprises one of a heart rate, a speed, a heart rate variability, a power, an acceleration, and a force.

10. The method of claim 1 wherein one of the multiple parameters comprises a biomechanical parameter, wherein the biomechanical parameter comprises one of vertical oscillation, leg power balance, arm power balance, power through range of motion, foot strike impact, time on ground, and foot strike pattern.

11. The method of claim 1 wherein the resistance parameter is based on a turnover parameter and wherein the turnover parameter comprises one of stride rate, cadence, and stroke rate.

12. The method of claim 1 wherein one of the multiple parameters comprises an environmental parameter, wherein the environmental parameter comprises one of temperature, humidity, and wind speed.

13. The method of claim 1 wherein the classifying further comprises comparing measurements of the received activity data against at least one lower threshold of the set and at least one upper threshold of the set.

14. The method of claim 1 further comprising:

accessing a training plan corresponding to the activity session, wherein the training plan indicates one or more activity types;

comparing at least some of the plurality of measurements of the received activity data to one or more measurements of the training plan; and

determining conformity with the training plan based on one or more of duration, distance, intensity, number of distinct activity types, number of instances of an activity type, and rest periods between activity types.

15. The method of claim 1 further comprising determining one or more physiological performance measures for the user based on the comparing, wherein the one or more physiological performance measures comprise one or more of cardiovascular, neurocardio, and muscular performance measures.

16. The method of claim 1 further comprising determining an overall performance measure in view of the comparing of the first segment of the received activity data and the second segment of historical data, wherein the overall performance measure is based on scores assigned to one or more of endurance, strength endurance, and speed.

17. A non-transitory tangible computer readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, cause the processor to:

receive activity data of one or more sensors that is indicative of an activity of a user, the activity data comprising a plurality of sensor measurements associated with multiple parameters monitored during an activity session;

retrieve a set of criteria that comprises thresholds to detect an activity type, wherein the set comprises a criterion related to a biomedical parameter and a criterion related to a resistance parameter;

classify, based on the set of criteria, the received activity data of the one or more sensors into one or more segments, wherein the one or more segments comprise a first segment comprising a portion of the activity data corresponding to the activity type;

access historical data associated with the user, the historical data comprising a second segment of historical data corresponding to the activity type;

compare the first segment comprising the portion of the received activity data and the second segment comprising historical data; and

generate output based on the comparing.

18. The non-transitory tangible computer readable storage medium of claim 17 wherein the instructions further cause the processor to determine a performance value for the activity type based on the comparing of the first segment and the second segment.

19. The non-transitory tangible computer readable storage medium of claim 18 wherein the output comprises an alert that is provided to the user during the activity session, wherein the activity session corresponds to a planned workout.

20. The non-transitory tangible computer readable storage medium of claim 19 wherein the alert comprises one or more of a performance measure based on the performance value for the activity type, an instruction to modify the activity of the activity session, or a modification to a training plan for the planned workout.

21. The non-transitory tangible computer readable storage medium of claim 18 wherein the biomedical parameter comprises at least one of electrocardiograph (ECG), blood pressure (BP), body temperature, oxygen saturation, respiration rate, energy expenditure, or oxygen uptake.

22. The non-transitory tangible computer readable storage medium of claim 18 wherein the resistance parameter comprises one of altitude, slope, gradient, stride rate, stroke rate, cadence, or distance per limb turnover.

23. The non-transitory tangible computer readable storage medium of claim 18 wherein the activity session comprises activities associated with a plurality of different activity types, and wherein the instructions further cause the processor to detect the plurality of activity types of the activity session by comparing the received activity data with a plurality of sets of criteria, wherein each of the plurality of sets corresponds to a multi-parameter zone for an activity type.

24. A system comprising:

a memory; and

a processor operatively coupled to the memory, the processor to execute instructions to;

receive activity data of one or more sensors that is indicative of an activity of a user, the activity data comprising a plurality of sensor measurements associated with multiple parameters monitored during an activity session;

retrieve a set of criteria that comprise thresholds to detect an activity type, wherein the set comprises a criterion related to a biomedical parameter and a criterion related to a resistance parameter;

classify, based on the set of criteria, the received activity data of the one or more sensors into one or more segments, wherein the one or more segments comprise a first segment comprising a portion of the activity data corresponding to the activity type;

access historical data associated with the user, the historical data comprising a second segment of historical data corresponding to the activity type;

compare the first segment comprising the portion of the received activity data and the second segment comprising historical data; and

generate output based on the comparing.

25. The system of claim 24 wherein the processor is further to determine a performance value for the activity type based on the comparing of the first segment and the second segment.

26. The system of claim 25 wherein the output comprises an alert that is provided to the user during the activity session, wherein the activity session corresponds to a planned workout.

27. The system of claim 26 wherein the alert comprises one or more of a performance measure based on the performance value for the activity type, an instruction to modify the activity of the user during the activity session, or a modification to a training plan for the planned workout.

28. The system of claim 24 wherein the biomedical parameter comprises at least one of electrocardiograph (ECG), blood pressure (BP), body temperature, oxygen saturation, respiration rate, energy expenditure, or oxygen uptake.

29. The system of claim 24 wherein the resistance parameter comprises one of altitude, slope, gradient, stride rate, stroke rate, cadence, or distance per limb turnover.

30. The system of claim 24 wherein the activity session comprises activities associated with a plurality of different activity types, and wherein the processor is further to detect the plurality of activity types of the activity session by comparing the received activity data with a plurality of sets of criteria, wherein each of the plurality of sets corresponds to a multi-parameter zone for an activity type.