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      Classification of human motion based on affective state descriptors

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      Author
      Cimen, G.
      Ilhan, H.
      Capin, T.
      Gurcay, H.
      Date
      2013
      Source Title
      Computer Animation and Virtual Worlds
      Print ISSN
      1546-4261
      Publisher
      John Wiley & Sons Ltd.
      Volume
      24
      Issue
      3-4
      Pages
      355 - 363
      Language
      English
      Type
      Article
      Item Usage Stats
      135
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      105
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      Abstract
      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.
      Keywords
      Affective state
      Emotion
      Human motion classification
      Motion capture
      Permalink
      http://hdl.handle.net/11693/28015
      Published Version (Please cite this version)
      http://dx.doi.org/10.1002/cav.1509
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      • Department of Computer Engineering 1413
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