This paper presents research using full body skeletal movements captured using video-based sensor technology developed by Vicon Motion Systems, to train a machine to identify different human emotions. The Vicon system uses a series of 6 cameras to capture lightweight markers placed on various points of the body in 3D space, and digitizes movement into x, y, and z displacement data. Gestural data from five subjects was collected depicting four emotions: sadness, joy, anger, and fear. Experimental results with different machine learning techniques show that automatic classification of this data ranges from 84% to 92% depending on how it is calculated. In order to put these automatic classification results into perspective a user study on the human perception of the same data was conducted with average classification accuracy of 93%. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Kapur, A., Kapur, A., Virji-Babul, N., Tzanetakis, G., & Driessen, P. F. (2005). Gesture-based affective computing on motion capture data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3784 LNCS, pp. 1–7). Springer Verlag. https://doi.org/10.1007/11573548_1
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