Automated physical activity classification

• US 9,665,873 B2
• Filed: 02/24/2011
• Issued: 05/30/2017
• Est. Priority Date: 02/24/2010
• Status: Active Grant

• Current Assignee: Pear Labs LLC
• Original Assignee: Performance Lab Technologies Ltd (Pear Sports, LLC)
• Inventors: Ackland, Jonathan Edward Bell, McMaster, Kerri Anne
• Primary Examiner(s): Galka, Lawrence
• Application Number:
  US13/581,062
• Publication Number:
  US 20130053990A1
• Time in Patent Office: 2,287 Days
• CPC Class Codes: A61B 2503/10 Athletes
• CPC Class Codes: A61B 2505/09 Rehabilitation or training
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• 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 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A63B 2220/00 Measuring of physical parameters relating to sporting activity
• CPC Class Codes: A63B 2225/50 : Wireless data transmission, e.g. by radio transmitters or telemetry
• CPC Class Codes: A63B 2230/00 : Measuring physiological parameters of the user
• CPC Class Codes: A63B 24/00 : Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
• 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: G06Q 30/02 : Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G16H 20/30 : relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients

Abstract

The invention relates to a system and method for analyzing an activity session. Data from a monitoring device worn by a user of the system for example is received over a period of time or distance and a classification system of the invention is utilized in real time or post session to determine the activities performed by the user during the session. The data collected relates to multiple parameters monitored during the activity session. The classification system defines an activity using a set of threshold criteria for a combination of parameters and therefore identifies a particular activity performed during the activity session when a combination of monitored parameters satisfies the threshold criteria for a particular activity. Determining the activities performed in this way allows for more useful interpretation of the data which in turn leads to more effective coaching advice and feedback for the user.

Claims

1. A method of analyzing an activity session comprising:

receiving activity data for an activity, the activity data corresponding to multiple parameters;

retrieving a set of threshold criteria for a particular user in view of the activity, the set of threshold criteria corresponding to the multiple parameters and at least one of the threshold criteria being based on an anaerobic threshold of the particular user, wherein the anaerobic threshold is determined by collecting activity data of the particular user prior to the activity session, forming a plurality of incidence bins containing numbers of incidences of the activity data within specific ranges, and identifying at least one incidence bin of the plurality of incidence bins with the highest change, wherein the anaerobic threshold is based on a range of the identified at least one incidence bin;

comparing the activity data with the set of threshold criteria to identify an effort of the particular user, wherein the effort is defined by a difference between the activity data and the threshold criteria based on the anaerobic threshold of the particular user;

determining a resistance experienced by the particular user, wherein the resistance comprises a measure of terrain;

classifying multiple durations of the activity into one or more predefined training type segments based on the resistance experienced by the particular user and the identified effort of the particular user; and

wherein the method is executed by one or more processors.

2. The method of claim 1, wherein receiving the activity data for the activity comprises receiving the activity data during the activity.

3. The method of claim 1, further comprising:

accessing a training plan comprising a duration for one of the one or more predefined training type segments;

comparing the duration from the training plan with a duration of the one or more predefined training type segments; and

generating a response for the particular user in view of the comparing the duration of the training plan.

4. The method of claim 3 further comprising, updating the training plan based on the comparison with the duration of the one or more predefined training type segments.

5. The method of claim 1, wherein the difference between the activity data and the threshold criteria corresponds to a percentage of the anaerobic threshold.

6. The method of claim 1, wherein the measure of terrain is based on a change in one or more of a slope, a gradient, or an altitude.

7. The method of claim 1, wherein the resistance comprises the measure of terrain and further comprises a body weight, a carried weight, or an arm/leg turnover, wherein the arm/leg turnover is defined by one or more of stride rate, change in stride rate, stroke rate, change in stroke rate, cadence, change in cadence, distance per arm/leg turnover, change in distance per arm/leg turnover.

8. The method of claim 1, wherein the activity data is received in multiple data streams and comprises data points, each of the multiple data streams relating to one of the multiple parameters.

9. The method of claim 1, wherein the activity data is processed in real time or is processed after the activity session, wherein the activity data comprises time, distance, or location stamped blocks of activity data.

10. The method of claim 9 further comprising, enabling the particular user to manually initiate the stamping of the stamped blocks of activity.

11. The method of claim 1, wherein the activity data is received from one or more activity monitoring devices comprising one or more sensing elements arranged to obtain data indicative of the multiple parameters monitored during the activity session.

12. The method of claim 11, wherein the one or more activity monitoring devices comprise a cellular device having one or more sensing elements or receivers for obtaining data indicative of the multiple parameters monitored during the activity session.

13. The method of claim 1, wherein the activity data is received in real time, post activity, or post activity session by one or more monitoring devices, either directly or via a communication network or both.

14. The method of claim 1, wherein an additional threshold criteria of the set of threshold criteria for the particular user is derived at least partly from one or more of a maximum threshold, historic data associated to the particular user, a threshold in a plan, an ideal zone or threshold, an arbitrary threshold, a derived effort level, a lactate test threshold, a Conconi test threshold, a ventilation test threshold, or an aerobic threshold of the particular user.

15. The method of claim 1, wherein the anaerobic threshold of the particular user indicates a maximum effort of the particular user during a predetermined period of exercise performed prior to the activity session.

16. The method of claim 1, wherein the effort and the anaerobic threshold of the particular user are based on at least one of heart rate data, speed data, or power data of the particular user.

17. The method of claim 1, wherein the anaerobic threshold of the particular user is further based on a ventilation test, a lactate test, a conconi test, or a maximum cardiovascular threshold of the particular user.

18. The method of claim 1, wherein the anaerobic threshold is determined by:

collecting activity data comprising heart rate data during exercise performed by the particular user prior to the activity session, wherein the heart rate data comprises a heart rate measurement, a heart rate variability, a heart rate change, a relaxation rate, or a change in relaxation rate;

forming the plurality of incidence bins that containing numbers of incidences of the heart rate data within specific ranges;

identifying the at least one incidence bin of the plurality of incidence bins with the highest change, wherein the at least one incidence bin corresponds to a heart rate data range; and

updating the anaerobic threshold based on the heart rate data range of the at least one incidence bin.

19. The method of claim 1, wherein the anaerobic threshold is determined by:

collecting activity data comprising power data during exercise performed by the particular user prior to the activity session;

forming the plurality of incidence bins that contain numbers of incidences of the power data within specific ranges;

identifying the at least one incidence bin of the plurality of incidence bins with the highest change, wherein the at least one incidence bin corresponds to a power range; and

updating the anaerobic threshold based on the power range of the at least one incidence bin.

20. The method of claim 1, wherein a threshold criteria of the set of threshold criteria is determined based on perceived exertion scale values provided by the particular user during a calibration workout.

21. The method of claim 1 further comprising, receiving, by the one or more processors, the anaerobic threshold of the particular user from the particular user or from another device.

22. The method of claim 1 further comprising, determining, by the one or more processors, the anaerobic threshold of the particular user.

23. A system for analyzing an activity session comprising:

a processor;

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

receive activity data for an activity, the activity data corresponding to multiple parameters;

retrieve a set of threshold criteria for a particular user in view of the activity, the set of threshold criteria corresponding to the multiple parameters and at least one of the threshold criteria being based on an anaerobic threshold of the particular user, wherein the anaerobic threshold is determined by collecting activity data of the particular user prior to the activity session, forming a plurality of incidence bins containing numbers of incidences of the activity data within specific ranges, and identifying at least one incidence bin of the plurality of incidence bins with the highest change, wherein the anaerobic threshold is based on a range of the identified at least one incidence bin;

compare the activity data with the set of threshold criteria to identify an effort of the particular user, wherein the effort is defined by a difference between the activity data and the threshold criteria based on the anaerobic threshold of the particular user; and

determine a resistance experienced by the particular user, wherein the resistance comprises a measure of terrain;

classify multiple durations of the activity into one or more predefined training type segments based on the resistance experienced by the particular user and the identified effort of the particular user.

24. The system of claim 23, wherein the anaerobic threshold of the particular user is further based on a ventilation test, a lactate test, a conconi test, or a maximum cardiovascular threshold of the particular user.

25. The system of claim 23, wherein the processor is further to:

access a training plan comprising a duration for one of the one or more predefined training type segments;

compare the duration from the training plan with a duration of the one or more predefined training type segments; and

generate a response for the particular user in view of the comparing the duration of the training plan.

26. The system of claim 25 further comprising, updating the training plan based on the comparison with the duration of the one or more predefined training type segments.

27. The system of claim 23, wherein to compare the activity data with the set of threshold criteria, the processor is to compare the activity data with an anaerobic threshold of the particular user to identify the effort of the user.

28. The system of claim 23, wherein the difference between the activity data and the threshold criteria corresponds to a percentage of the anaerobic threshold.

29. The system of claim 23, wherein the measure of terrain is based on a change in one or more of a slope, a gradient, or an altitude.

30. The system of claim 23, wherein the resistance comprises the measure of terrain and further comprises a body weight, a carried weight, or an arm/leg turnover, wherein the arm/leg turnover is defined by one or more of stride rate, change in stride rate, stroke rate, change in stroke rate, cadence, change in cadence, distance per arm/leg turnover, change in distance per arm/leg turnover.

31. The system of claim 23, wherein the anaerobic threshold of the particular user indicates a maximum effort of the particular user during a predetermined period of exercise performed prior to the activity session.

32. The system of claim 23, wherein the effort and the anaerobic threshold of the particular user are based on at least one of heart rate data, speed data, or power data of the particular user.

33. The system of claim 23 wherein the anaerobic threshold of the particular user is further based on a ventilation test, a lactate test, a conconi test, or a maximum cardiovascular threshold of the particular user.

34. The system of claim 23, wherein the processor is further to:

collect activity data comprising heart rate data during exercise performed by the particular user prior to the activity session, wherein the heart rate data comprises a heart rate measurement, a heart rate variability, a heart rate change, a relaxation rate, or a change in relaxation rate;

form the plurality of incidence bins that contain numbers of incidences of the heart rate data within specific ranges;

identify the at least one incidence bin of the plurality of incidence bins with the highest change, wherein the at least one incidence bin corresponds to a heart rate data range; and

update the anaerobic threshold based on the heart rate data range of the at least one incidence bin.

35. The system of claim 23, wherein the processor is further to:

collect activity data comprising power data during exercise performed by the particular user prior to the activity session;

form the plurality of incidence bins that contain numbers of incidences of the power data within specific ranges;

identify the at least one incidence bin of the plurality of incidence bins with the highest change, wherein the at least one incidence bin corresponds to a power range; and

update the anaerobic threshold based on the power range of the at least one incidence bin.

36. The system of claim 23, wherein a threshold criteria of the set of threshold criteria is determined based on perceived exertion scale values provided by the particular user during a calibration workout.

37. The system of claim 23, wherein the processor is further to receive the anaerobic threshold of the particular user from the particular user or from another device.

38. The system of claim 23, wherein to receive the activity data for the activity comprises the processor to receive the activity data during the activity.

39. A non-transitory computer readable media comprising instructions that cause a processor to:

receive activity data during an activity, the activity data corresponding to multiple parameters;

retrieve a set of threshold criteria for a particular user in view of the activity, the set of threshold criteria corresponding to the multiple parameters and at least one of the threshold criteria being based on an anaerobic threshold of the particular user, wherein the anaerobic threshold is determined by collecting activity data of the particular user prior to the activity session, forming a plurality of incidence bins containing numbers of incidences of the activity data within specific ranges, and identifying at least one incidence bin of the plurality of incidence bins with the highest change, wherein the anaerobic threshold is based on a range of the identified at least one incidence bin;

compare the activity data with the set of threshold criteria to identify an effort of the particular user, wherein the effort is a difference between the activity data and the threshold criteria based on the anaerobic threshold of the particular user; and

determine a resistance experienced by the particular user, wherein the resistance comprises a measure of terrain;

classify multiple durations of the activity into one or more predefined training type segments based on the resistance experienced by the particular user and the identified effort of the particular user.

40. The non-transitory computer readable media of claim 39, wherein the anaerobic threshold of the particular user is further based on a ventilation test, a lactate test, a conconi test, or a maximum cardiovascular threshold of the particular user.

41. The non-transitory computer readable media of claim 39, wherein the processor is further to:

access a training plan comprising a duration for one of the one or more predefined training type segments;

compare the duration from the training plan with a duration of the one or more predefined training type segments; and

generate a response for the particular user in view of the comparing the duration of the training plan.

42. The non-transitory computer readable media of claim 41, wherein the processor is further to update the training plan based on the comparison with the duration of the one or more predefined training type segments.

43. The non-transitory computer readable media of claim 39, wherein the difference between the activity data and the threshold criteria corresponds to a percentage of the anaerobic threshold.

44. The non-transitory computer readable media of claim 39, wherein the measure of terrain is based on a change in one or more of a slope, a gradient, or an altitude.

45. The non-transitory computer readable media of claim 39, wherein the resistance comprises the measure of terrain and further comprises a body weight, a carried weight, or an arm/leg turnover, wherein the arm/leg turnover is defined by one or more of stride rate, change in stride rate, stroke rate, change in stroke rate, cadence, change in cadence, distance per arm/leg turnover, change in distance per arm/leg turnover.

46. The non-transitory computer readable media of claim 39, wherein the anaerobic threshold of the particular user indicates a maximum effort of the particular user during a predetermined period of exercise performed prior to the activity session.

47. The non-transitory computer readable media of claim 39, wherein the effort and the anaerobic threshold of the particular user are based on at least one of heart rate data, speed data, or power data of the particular user.

48. The non-transitory computer readable media of claim 39, wherein the anaerobic threshold of the particular user is further based on a ventilation test, a lactate test, a conconi test, or a maximum cardiovascular threshold of the particular user.

49. The non-transitory computer readable media of claim 39, wherein the processor is further to:

collect activity data comprising heart rate data during exercise performed by the particular user prior to the activity session, wherein the heart rate data comprises a heart rate measurement, a heart rate variability, a heart rate change, a relaxation rate, or a change in relaxation rate;

form the plurality of incidence bins that contain numbers of incidences of the heart rate data within specific ranges;

identify the at least one incidence bin of the plurality of incidence bins with the highest change, wherein the at least one incidence bin corresponds to a heart rate data range; and

update the anaerobic threshold based on the heart rate data range of the at least one incidence bin.

50. The non-transitory computer readable media of claim 39, wherein the processor is further to:

collect activity data comprising power data during exercise performed by the particular user prior to the activity session;

form the plurality of incidence bins that contain numbers of incidences of the power data within specific ranges;

identify the at least one incidence bin of the plurality of incidence bins with the highest change, wherein the at least one incidence bin corresponds to a power range; and

update the anaerobic threshold based on the power range of the at least one incidence bin.

51. The non-transitory computer readable media of claim 39, wherein a threshold criteria of the set of threshold criteria is determined based on perceived exertion scale values provided by the particular user during a calibration workout.

52. The non-transitory computer readable media of claim 39, wherein the processor is further to receive the anaerobic threshold of the particular user from the particular user or from another device.