Abstract
The main goal of this study is to classify affective laughter expressions from body movements. Using a non-intrusive Kinect sensor, body movement data from laughing participants were collected, annotated and segmented. A set of features that include the head, torso, shoulder movements, as well as the positions of the right and left hands, were used by a decision tree classifier to determine the type of emotions expressed in the laughter. The decision tree classifier performed with an accuracy of 71.02% using a minimum set of body movement features.
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Cu, J., Luz, M.B., Nocum, M., Purganan, T.J., Wong, W.S. (2017). Affective Laughter Expressions from Body Movements. In: Numao, M., Theeramunkong, T., Supnithi, T., Ketcham, M., Hnoohom, N., Pramkeaw, P. (eds) Trends in Artificial Intelligence: PRICAI 2016 Workshops. PRICAI 2016. Lecture Notes in Computer Science(), vol 10004. Springer, Cham. https://doi.org/10.1007/978-3-319-60675-0_12
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DOI: https://doi.org/10.1007/978-3-319-60675-0_12
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