Abstract: A system, method, and computer program product for analyzing force sensor data is provided. Force sensor data collected from a plurality of force sensors positioned underfoot is analyzed to detect foot contact events and/or a foot contact period. Foot contact and/or foot off can be detected based on inflection points identified in the force signal data received from the plurality of sensors. Identifying foot contact events by detecting inflection points in the force sensor data can increase the sensitivity of detecting both foot contact and foot off. The use of inflection points also allows both foot contact and foot off to be identified even when these foot contact events occur at different force signal heights. Methods for determining ground reaction force data and correcting the magnitude of ground reaction force signals are also provided.

Abstract: A system, method and computer program product for determining cycling power. A plurality of force sensor readings are acquired from force sensors positioned underfoot. At least one revolution is identified. Force values are determined for the at least one revolution using aggregate force data. The cycling cadence, foot velocity, and foot angle are determined for each revolution. User mass is also determined. The cycling power associated with the force sensor readings is determined by inputting the force values, the cycling cadence, the foot velocity, and the foot angle, and the user mass to a machine learning model trained to predict the cycling power. The cycling power can then be provided to the user as feedback or stored for purposes such as later review and analysis. The inputs to the machine learning model can be determined entirely based off of sensor data received from a wearable device worn by the user.

Abstract: A system, method and computer program product for determining mechanical running power. A plurality of sensor readings is acquired from a plurality of force sensors positioned underfoot. Force values are determined for a plurality of strides using aggregate force data. The slope, a stance time and running speed are determined for each stride. The mechanical running power associated with the plurality of sensor readings is determined by inputting the force values, the slope, the stance time and the running speed to a machine learning model trained to predict the mechanical running power. The mechanical running power can then be provided to the user as feedback or stored for purposes such as later review and analysis. The inputs to the machine learning model can be determined entirely based off of sensor data received from a wearable device worn by the user.