Automated prescription of activity based on physical activity data

Inventors

Ackland, Jonathan Edward Bell

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.

Core Innovation

The disclosed exercise/activity coaching system receives activity data of one or more sensors indicative of an activity of a user. The activity data comprises a plurality of sensor measurements associated with multiple parameters monitored during an activity session, and the system retrieves a set of criteria that comprises thresholds to detect an activity type, including a criterion related to a biomedical parameter and a criterion related to a resistance parameter.

The system classifies the received activity data into one or more segments where a first segment corresponds to the activity type. It accesses historical data associated with the user, where the historical data includes a second segment corresponding to the activity type, and compares the first segment with the second segment. The comparing supports an assessment grounded in the multi-parameter activity type segments rather than a single-parameter training zone concept.

In addition to generating output based on the comparison, the system can compute a performance value for the activity type derived from the comparing. The generated output can include alerting a user during a planned workout activity session, and the system may also detect multiple activity types in one activity session by using multiple sets of criteria, each corresponding to a multi-parameter zone for an activity type.

Claims Coverage

The independent claims are method (clm-00001), non-transitory storage medium with executable instructions (clm-00017), and a system (clm-00024). Across these independent claims, there are five main inventive features: multi-parameter sensor measurement reception, threshold-based activity-type detection using biomedical and resistance parameter criteria, classification into a first current segment, retrieval of a second historical segment, and comparison of the segments to generate output.

The independent claims consistently cover a criteria-threshold, multi-parameter activity-type classification approach that segments a received activity session, retrieves corresponding historical segments, compares the two, and generates output based on that comparison. Dependent claim themes refine the output toward performance values and user alerts, and expand detection using multiple criteria sets tied to multi-parameter zones.

Stated Advantages

Documented Applications