Abstract: An avatar personalized for a subject and used in movement analysis and coaching uses a 3D polygon mesh representation of a human, authored for one human skeleton model, with a different size (scaled) skeleton model, without producing visual artifacts. Dimensions of the scaled skeleton model can be determined from three or eight measurements of a subject or from scanning or photographic methods. The avatar animated based on motion capture data may be animated alone or with one or more other avatars that are synchronized spatially, orientationally, and/or at multiple times to allow a user to easily compare differences between performances. An information presentation in an animation may use pixel energies from multiple animation frames to ensure that the information remains relatively stationary and does not obstruct important visual details.

Abstract: An avatar personalized for a subject and used in movement analysis and coaching uses a 3D polygon mesh representation of a human, authored for one human skeleton model, with a different size (scaled) skeleton model, without producing visual artifacts. Dimensions of the scaled skeleton model can be determined from three or eight measurements of a subject or from scanning or photographic methods. The avatar animated based on motion capture data may be animated alone or with one or more other avatars that are synchronized spatially, orientationally, and/or at multiple times to allow a user to easily compare differences between performances. An information presentation in an animation may use pixel energies from multiple animation frames to ensure that the information remains relatively stationary and does not obstruct important visual details.

Abstract: A motion capture suit includes a top or pants, a sensor socket fixed to a portion of the top or the pants about a limb, and a sensor inserted in the sensor socket. The sensor includes a measurement device having measurement axes that are offset by about 45 degrees from a long axis of the limb.

Abstract: A method for a processor to analyze motion data includes receiving times series of raw motion data, hereafter the raw data streams, and generating sparse data streams based on the raw data streams. The method also includes performing a raw data action identification to detect the action in a window of time, which includes modifying one or more first thresholds based on the user information and comparing one or more of the raw data streams in the window with one or more modified first thresholds. The method further includes determining a score of the detected action and providing a visualized feedback to the user based on the score.