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Classification of human motion based on affective state descriptors
Author(s) -
Cimen Gokcen,
Ilhan Hacer,
Capin Tolga,
Gurcay Hasmet
Publication year - 2013
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.1509
Subject(s) - computer science , motion (physics) , artificial intelligence , basis (linear algebra) , human motion , motion capture , pattern recognition (psychology) , measure (data warehouse) , data mining , mathematics , geometry
Human body movements and postures carry emotion‐specific information. On the basis of this motivation, the objective of this study is to analyze this information in the spatial and temporal structure of the motion capture data and extract features that are indicative of certain emotions in terms of affective state descriptors. Our contribution comprises identifying the directly or indirectly related descriptors to emotion classification in human motion and conducting a comprehensive analysis of these descriptors (features) that fall into three different categories: posture descriptors, dynamic descriptors, and frequency‐based descriptors in order to measure their performance with respect to predicting the affective state of an input motion. The classification results demonstrate that no single category is sufficient by itself; the best prediction performance is achieved when all categories are combined. Copyright © 2013 John Wiley & Sons, Ltd.