Activity Classification Based on Classification of Repetition Regions
First Claim
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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.
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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.
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Citations
20 Claims
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1. A method comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method comprising:
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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. - View Dependent Claims (14, 15, 16, 17, 18, 19)
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20. A non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:
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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.
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Specification