Abstract
Many areas of today’s society are seeing an increased importance in the creation of systems capable of interacting with users on an affective level through a variety of modalities. Our focus has been on affective posture recognition. However, a deeper understanding of the relationship between emotions in terms of postural expressions is required. The goal of this study was to identify affective dimensions that human observers use when discriminating between postures, and to investigate the possibility of grounding this affective space into a set of posture features. Using multidimensional scaling, arousal, valence, and action tendency were identified as the main factors in the evaluation process. Our results showed that, indeed, low-level posture features could effectively discriminate between the affective dimensions.
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Kleinsmith, A., De Silva, P.R., Bianchi-Berthouze, N. (2005). Grounding Affective Dimensions into Posture Features. In: Tao, J., Tan, T., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2005. Lecture Notes in Computer Science, vol 3784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573548_34
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DOI: https://doi.org/10.1007/11573548_34
Publisher Name: Springer, Berlin, Heidelberg
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