Affective laughter expressions from body movements
College
College of Computer Studies
Department/Unit
Computer Technology
Document Type
Conference Proceeding
Source Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
10004 LNAI
First Page
139
Last Page
145
Publication Date
1-1-2017
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. © Springer International Publishing AG 2017.
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Digitial Object Identifier (DOI)
10.1007/978-3-319-60675-0_12
Recommended Citation
Cu, J., Luz, M. L., Nocum, M. D., Purganan, T., & Wong, W. (2017). Affective laughter expressions from body movements. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10004 LNAI, 139-145. https://doi.org/10.1007/978-3-319-60675-0_12
Disciplines
Computer Sciences
Keywords
Human activity recognition; Pattern recognition systems; Laughter--Data processing; Emotion recognition
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