Estimating Gesture Accuracy in Motion-Based Health Games


Autoria(s): Barrett, Christian; Brown, Jacob; Hartford, Jay; Hoerter, Michael; Kennedy, Andrew; Hassan, Ray; Whittinghill, David
Data(s)

17/10/2014

01/12/2014

Resumo

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 three-dimensional 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.

Identificador

urn:nbn:de:0009-6-40200

http://www.jvrb.org/past-issues/11.2014/4020

Idioma(s)

eng

Direitos

DPPL

Fonte

Journal of Virtual Reality and Broadcasting ; 11(2014) , 8

Palavras-Chave #004 #http://dewey.info/class/004/ #KINECT #RGB-D camera #algorithms #application development #cerebral palsy #health games #physical therapy #serious games #swd: Cerebral palsy #swd: Physiotherapie #swd: Applikation <Software>