Abstract: A system receives a pose sequence from a client device via a communication network. The pose sequence is a time series representation of a user performing a movement. The network system identifies various musculoskeletal key-points. The key-points describe the musculoskeletal structure of the user as she performs the movement. The system creates a data structure describing the time evolution of the key-points that represents the movement. The system inputs the data structure into a machine learning model to determine contextual feedback for the movement. To do so, the system compares the movement data structure to historical movement data structures and generates feedback for the movement based on differences between the input movement and the historical movement. The system generates the feedback according to user preferences.

Abstract: A system identifies a movement and generates prescriptive analytics of that movement. To identify a movement, a system accesses an image of an observation volume where users execute movements. The system identifies a location including an active region in the image. The active region includes a movement region and a surrounding region. The system identifies musculoskeletal points of a user in the location and determines when the user enters the active area. The system identifies a movement of a user in the active region based on the time evolution of key-points in the active region. The system determines descriptive analytics describing the movement. Based on the descriptive analytics, the system generates prescriptive analytics for the movement and provides the prescriptive analytics to the user. The prescriptive analytics may inform future and/or current movements of the user.

Abstract: A system identifies a movement and generates prescriptive analytics of that movement. To identify a movement, a system accesses an image of an observation volume where users execute movements. The system identifies a location including an active region in the image. The active region includes a movement region and a surrounding region. The system identifies musculoskeletal points of a user in the location and determines when the user enters the active area. The system identifies a movement of a user in the active region based on the time evolution of key-points in the active region. The system determines descriptive analytics describing the movement. Based on the descriptive analytics, the system generates prescriptive analytics for the movement and provides the prescriptive analytics to the user. The prescriptive analytics may inform future and/or current movements of the user.