Activity Classification Based on Classification of Repetition Regions

• US 20160249832A1
• Filed: 02/27/2015
• Published: 09/01/2016
• Est. Priority Date: 02/27/2015
• Status: Abandoned Application

• Current Assignee: Amiigo, Inc.
• Original Assignee: Amiigo, Inc.
• Inventors: Carter, Abraham Luke, Scott, David Jennings, Azarnasab, Ehsan
• Application Number:
  US14/634,685
• Publication Number:
  US 20160249832A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/1118 Determining activity level
• 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 5/4519 Muscles A61B5/389, A61B5/224 take precedence
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• 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

Abstract

A wearable device allows the tracking of human movements during activity, such as while exercising or playing a sport. To improve user experience with the wearable device, an activity classification server classifies activities and repetitions of the activity. To further improve user experience, the activity classification server can prompt the user to perform an activity and identify the activity while the user performs the prompted activity. The user can also indicate to the device an election to perform a particular activity without being prompted by the device and the user can classify the activity by indicating to the device what activity the user will perform.

Claims

1. A method comprising:

receiving, by an activity classification server, raw data from a plurality of activity-tracking devices worn by a user while performing an activity, the raw data comprising at least one activity region with one or more points, each point associated with a time stamp and amplitude;

identifying an activity region in the raw data, the activity region comprising at least a threshold number of points associated with amplitudes that exceed a first threshold amplitude within an interval of time;

identifying a set of feature points in the activity region in the raw data, a feature point in the set of feature points associated with an amplitude that exceeds a second threshold amplitude, at least one feature point in the set of feature points similar in amplitude to a second feature point in the set of feature points;

determining a repetition (“

rep”

) region in the activity region in the raw data based on the set of feature points, the rep region including the at least one feature point and not including the second feature point;

classifying the rep region as a repetition type, a repetition type being a movement associated with an activity.

2. The method of claim 1, further comprising:

performing a preliminary analysis on the received raw data to identify repetitive regions, a repetitive region associated with at least a threshold interval of time and comprising a threshold number of points repeated in a second threshold interval of time in the raw data.

3. The method of claim 2, further comprising:

presenting the user with information based on the preliminary analysis, the information describing change in muscle fatigue over the repetitive regions, change in resistance between repetitive regions, or any combination thereof.

4. The method of claim 2, further comprising:

presenting the user with information based on the preliminary analysis, the information describing suggestions based on changes between repetitive regions, or any combination thereof.

5. The method of claim 1, wherein an identified feature point associated with a time stamp is further identified based on a second feature point associated with a second time stamp that is an interval from the time stamp.

6. The method of claim 1, wherein determining a rep region in the activity region in the raw data based on the set of feature points further comprises:

determining a number of associations between various feature points in the set of feature points; and

responsive to the number of associations exceeding a threshold number of associations, determining a region in the activity region in the raw data including at least a feature point in the various feature points as the rep region.

7. The method of claim 6, wherein the rep region is classified as a repetition type based on the number of associations.

8. The method of claim 1, wherein classifying the rep region as a repetition type comprises:

comparing feature points in the rep region with a reference database, the reference database comprising associations between rep regions previously performed by a plurality of users of the activity classification server and determined classifications of the rep regions.

9. The method of claim 1, further comprising:

prompting the user wearing the plurality of activity-tracking devices to record test data for an activity about to be performed by the user for a predetermined interval of time;

receiving preliminary raw test data tracking the prompted activity;

prompting the user to classify the prompted activity; and

storing the classification and the preliminary raw data in a reference database.

10. The method of claim 9, wherein classifying the rep region as a repetition type comprises:

comparing feature points in the rep region with the reference database.

11. The method of claim 1, further comprising:

associating the activity region in the raw data with an activity type based on the classified rep region as the repetition type.

12. The method of claim 1, further comprising:

determining that an association of the activity region cannot be made based on the classified rep region;

determining an additional rep region in the activity region in the raw data based on the set of feature points;

classifying the additional rep region as a second repetition type; and

associating the activity region in the raw data with an activity type based on the classified rep region as the repetition type and the classified additional rep region as the second repetition type.

13. A method comprising:

receiving, by an activity classification server, raw data from a plurality of activity-tracking devices worn by a user while performing an activity, wherein the raw data comprises one or more points, each point associated with a time stamp and an amplitude exceeding a first threshold amplitude;

identifying a set of feature points in the raw data, a feature point in the set of feature points associated with an amplitude that exceeds a second threshold amplitude, at least one feature point in the set of feature points similar in amplitude to a second feature point in the set of feature points;

determining a repetition (“

rep”

) region in the raw data based on the set of feature points, the rep region including the at least one feature point and not including the second feature point;

classifying the rep region as a repetition type, a repetition type being a movement associated with an activity; and

associating the rep region with an activity type based on the classified rep region as the repetition type.

14. The method of claim 13, wherein an identified feature point associated with a time stamp is further identified based on a second feature point associated with a second time stamp that is an interval from the time stamp.

15. The method of claim 13, wherein determining a rep region in the raw data based on the set of feature points further comprises:

determining a number of associations between various feature points in the set of feature points; and

responsive to the number of associations exceeding a threshold number of associations, determining a region in the raw data including at least a feature point in the various feature points as the rep region.

16. The method of claim 15, wherein the rep region is classified as a repetition type based on the number of associations.

17. The method of claim 13, wherein classifying the rep region as a repetition type comprises:

comparing feature points in the rep region with a reference database, the reference database comprising associations between rep regions previously performed by a plurality of users of the activity classification server and determined classifications of the rep regions.

18. The method of claim 13, further comprising:

prompting the user wearing the plurality of activity-tracking devices to record test data for a repetition of an activity type about to be performed by the user for a predetermined interval of time;

receiving preliminary raw test data tracking the prompted repetition;

prompting the user to classify the prompted repetition as a repetition type; and

storing the classification and the preliminary raw data in a reference database.

19. The method of claim 18, wherein classifying the rep region as a repetition type comprises:

comparing feature points in the rep region with the reference database.

20. A non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:

receive raw data from a plurality of activity-tracking devices worn by a user while performing an activity, wherein the raw data comprises one or more points, each point associated with a time stamp and an amplitude exceeding a first threshold amplitude;

identify a set of feature points in the raw data, a feature point a point in the set of feature points associated with an amplitude that exceeds a second threshold amplitude, at least one feature point in the set of feature points similar in amplitude with a second feature point in the set of feature points;

determine a repetition (“

rep”

) region in the raw data based on the set of feature points, the rep region including the at least one feature point and not including the second feature point;

classify the rep region as a repetition type, a repetition type being a movement associated with an activity; and

associate the rep region with an activity type based on the classified rep region as the repetition type.