Abstract: A system generates applications based on a domain specific language (DSL). The applications allow a user to take measurements based on user movements using a client device (for example, a mobile phone) for providing to an expert. The domain specific language includes commands that cause the generated applications to perform various tasks, such as providing specific instructions to the user, starting and stopping recording, taking measurements, and so on. The system compiles the DSL specification to generate executable instructions that are deployed (for example, on a client device such as a mobile phone). The DSL specification allows users to specify virtual points based on multiple landmarks obtained from the images of the user. The DSL specification allows users to specify a point as a stable point for landmarks that are expected to be stationary but move across frames.

Abstract: A system generates applications based on a domain specific language (DSL). The applications allow a user to take measurements based on user movements using a client device (for example, a mobile phone) for providing to an expert. The domain specific language includes commands that cause the generated applications to perform various tasks, such as providing specific instructions to the user, starting and stopping recording, taking measurements, and so on. The system compiles the DSL specification to generate executable instructions that are deployed (for example, on a client device such as a mobile phone). The DSL specification allows users to specify virtual points based on multiple landmarks obtained from the images of the user. The DSL specification allows users to specify a point as a stable point for landmarks that are expected to be stationary but move across frames.

Abstract: An exercise feedback system receives exercise data such as images or video captured by client devices of users performing exercises. The exercise feedback system may access a machine learning model trained using image of a population of users. The images used for training may be labeled, for example, as having proper or improper musculoskeletal form. The exercise feedback system may determine a metrics describing the musculoskeletal form of a user by applying the trained machine learning model to images of the user as input features. The exercise feedback system may generate feedback for a certain exercise using the metrics based on output predictions of the model. The feedback can be provided to a client device of the user or a physical therapist for presentation.

Abstract: An exercise feedback system receives exercise data such as images or video captured by client devices of users performing exercises. The exercise feedback system may access a machine learning model trained using image of a population of users. The images used for training may be labeled, for example, as having proper or improper musculoskeletal form. The exercise feedback system may determine a metrics describing the musculoskeletal form of a user by applying the trained machine learning model to images of the user as input features. The exercise feedback system may generate feedback for a certain exercise using the metrics based on output predictions of the model. The feedback can be provided to a client device of the user or a physical therapist for presentation.

Abstract: Systems and methods for enrolling nodes into an ad hoc network associated with a multi-roomed structure. Nodes within the ad hoc network comprise a communication module configured to communicate with the ad hoc network using at least one of room-limited communications and room-transparent communications.