Abstract
The purpose of this study was to determine the cardiovascular and behavioral patterns to develop a new algorithm of emotion recognition system through only behavioral patterns. However, to achieve this we must compare the features with both cardiovascular responses and subjective evaluations. Seven students were asked to wear PPG sensors and carry their smartphones to track locations and periodically evaluate subjective emotions. The social emotions were categorized into mutuality and sociality dimensions. As a result, in sociality, cardiovascular features implied significant patterns in 8 cardiovascular features (p < 0.01). In mutuality, significant patterns were implied only in total power (p < 0.01). Additionally, results for sociality in behavioral features implied significant patterns in transition time and total distance (p < 0.01). Cardiovascular and behavioral patterns are two factors that can determine the physiological effects of individuals according to emotions.
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Acknowledgments
This work was supported by the ICT R&D program of MSIP/IITP. [2015-0-00312, The development of technology for social life logging based on analyzing social emotion and intelligence of convergence contents].
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Lee, H., Jo, Y., Kim, H., Whang, M. (2018). Patterns of Cardiovascular and Behavioral Movements in Life-Logging According to Social Emotions. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_219
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DOI: https://doi.org/10.1007/978-981-10-7605-3_219
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