Abstract
Technology for sensing and understanding human behavior is being developed. We focused on the universality of Laban Movement Analysis, a body representation theory, in estimating internal states, such as emotions, and defined original feature values based on that theory. The feature values have shown their effectiveness in estimating emotions in a paired electronic instrument-making task and in a design-making task on a PC. In this study, to generalize further these features, we analyzed whether forward/backward or left/right movements contributed to emotion estimation. For this purpose, we defined new features and evaluated them by machine learning emotion estimation. For the body movement measurement experiment, we designed a video-watching task with few contrasting movements and a bespoke task with various movements. The results showed that the left-right direction feature was effective in the bias (Space) feature of the motion, and that the examination of the front-back and left-right directions improved the estimation’s accuracy.
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Fujiwara, S., Kobayashi, F., Aoyagi, S., Yamamoto, M. (2023). Features Focusing on the Direction of Body Movement in Emotion Estimation Based on Laban Movement Analysis. In: Kurosu, M., Hashizume, A. (eds) Human-Computer Interaction. HCII 2023. Lecture Notes in Computer Science, vol 14012. Springer, Cham. https://doi.org/10.1007/978-3-031-35599-8_12
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DOI: https://doi.org/10.1007/978-3-031-35599-8_12
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