Abstract
Emotion recognition is seen to be important not only for computer science or sport activity but also for old and sick people to live independently in their own homes as long as possible. In this paper Empatica E4 wristband is used to collect the date and assess the stress level of the user. We describe an algorithm for the classification of physiological data for emotion recognition. The algorithm has been divided into the following steps: data acquisition, signal preprocessing, feature extraction, and classification. The data acquired during various daily activities consist of more than 3 h of wristband signal. Through various stress tests we achieve a maximum accuracy of 71% for a stressed/relaxed classification. These results lead to the conclusion that Empatica E4 wristband can be used as a device for emotion recognition.
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Acknowledgement
Research and development activities leading to this article have been supported by the German Federal Ministry of Education and Research within the project “Cognitive Village: Adaptively Learning Technical Support System for Elderly” (Grant Number: 16SV7223K).
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Gouverneur, P., Jaworek-Korjakowska, J., Köping, L., Shirahama, K., Kleczek, P., Grzegorzek, M. (2017). Classification of Physiological Data for Emotion Recognition. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10245. Springer, Cham. https://doi.org/10.1007/978-3-319-59063-9_55
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