This manuscript details a technique for estimating gesture accuracy within the context of motion-based health video games using the MICROSOFT KINECT. We created a physical therapy game that requires players to imitate clinically significant reference gestures. Player performance is represented by the degree of similarity between the performed and reference gestures and is quantified by collecting the Euler angles of the player’s gestures, converting them to a threedimensional vector, and comparing the magnitude between the vectors. Lower difference values represent greater gestural correspondence and therefore greater player performance. A group of thirty-one subjects was tested. Subjects achieved gestural correspondence sufficient to complete the game’s objectives while also improving their ability to perform reference gestures accurately.


This is the publisher PDF of 19. Barrett, C., Brown, J., Hartford, J., Hoerter, M., Kennedy, A., Hassan, R. & Whittinghill, D. (2014). Estimating gesture accuracy in motion-based health games. Journal of Virtual Reality and Broadcasting (online). Published by DiPP-NRW, the version of record is at https://www.jvrb.org/past-issues/11.2014/4020.


KINECT, RGB-D camera, physical therapy, cerebral palsy, serious games, health games, algorithms, application development

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