Abstract
This research aims to present a method for emotion recognition using the K-Emocon dataset (Park et al. in Sci Data 7(1):1–16 [8]) for use in the healthcare sector as well as to enhance computer–human interaction. In the following work, we use peripheral physiological signals to recognize emotion using classifier models with multidimensional emotion space models. These signals are collected using IoT-based wireless wearable devices. Emotions are measured in terms of arousal and valence by using physiological signals obtained from these devices. Several machine learning models were used for emotion recognition. Thirty-eight input features were extracted from a variety of physiological signals present in the dataset for analysis. Best accuracy achieved for valence and arousal in our experiment was 91.12% and 62.19%, respectively. This study targets recognition and classification of emotions during naturalistic conversations between people using peripheral physiological signals. It is shown that it is viable to recognize emotions using these signals.
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Gupta, P., Balaji, S.A., Jain, S., Yadav, R.K. (2022). Emotion Recognition During Social Interactions Using Peripheral Physiological Signals. In: Smys, S., Bestak, R., Palanisamy, R., Kotuliak, I. (eds) Computer Networks and Inventive Communication Technologies . Lecture Notes on Data Engineering and Communications Technologies, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-16-3728-5_8
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DOI: https://doi.org/10.1007/978-981-16-3728-5_8
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