Automated motion of interest recognition, detection and self-learning

Abstract

A motion monitoring system can detect and identify motions of interest in motion data. The motion monitoring system can identify instances of the motion of interest in the motion data and can train a machine-learned model for identifying or characterizing motions of interest in motion data based on extracted features from the motions of interest. The motion monitoring system can also monitor motion data from a user and can help the user alter the performance of the motions of interest. For example, the motion monitoring system can generate an improvement strategy that helps the user improve their performance of the motion of interest. The motion monitoring system's altering of a user's performance of a motion of interest can be applied to physical therapy, gunfire detection, workplace motion improvement, sports, and driving.

Description

CROSS REFERENCE TO RELATED APPLICATION[0001]This application claims the benefit of U.S. Provisional Application No. 62 / 211,513, filed on Aug. 28, 2015, U.S. Provisional Application No. 62 / 213,003, filed Sep. 1, 2015, U.S. Provisional Application No. 62 / 293,658, filed Feb. 10, 2016, U.S. Provisional Application No. 62 / 338,016, filed May 18, 2016, and U.S. Provisional Application No. 62 / 355,528, filed Jun. 28, 2016, each of which is herein incorporated by reference in its entirety.[0002]This application also is related to and incorporates by reference in its entirety U.S. patent application Ser. No. ______, filed 26 Aug. 2016, titled “System and Method for Automatically Time Labelling Repetitive Data”.BACKGROUND[0003]Field of the Invention[0004]This disclosure relates generally to movement identification and more specifically to identifying motions of interest in signal data.[0005]Description of Art[0006]Sensors, such as accelerometers, can be used to generate signals of physical moti...

AI Technical Summary

Benefits of technology

The patent is about a system that can watch a user's movements and help them improve their performance. It can identify specific movements and create a model of the user's actions. This model can be compared to a reference model to determine ways to make the user better. The system can also automatically generate accurate models based on data from multiple users, and can filter out noise from the data. Overall, this system can help users learn and improve their movements more effectively.

Problems solved by technology

Conventionally, it is difficult to identify a motion of interest (MOI) in sensor signals.

Motions of interest can be difficult to detect because the sensor signals can often contain noise that masks the portions of the signal that represent the motion of interest.

However, these models often require humans to hand label sensor signals, which is time and resource intensive.

Thus, these models either require significant resources to develop and continue training, or are trained on small training data sets, which reduces the accuracy of the models.

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