Most of the existing studies focus on physical activities recognition, such as running, cycling, swimming, etc. But what affects our health, it is not only physical activities, it is also emotional states that we experience throughout the day. These emotional states build our behavior and affect our physical health significantly. Therefore, emotion recognition draws more and more attention of researchers in recent years. In this paper, we propose a system that uses off-the-shelf wearable sensors, including heart rate, galvanic skin response, and body temperature sensors to read physiological signals from the users and applies machine learning techniques to recognize their emotional states. We consider three types of emotional states and conduct experiments on real-life scenarios with ten users. Experimental results show that the proposed system achieves high recognition accuracy.
|Title of host publication
|Genetic and Evolutionary Computing - Proceedings of the Eleventh International Conference on Genetic and Evolutionary Computing, ICGEC 2017, November 6-8, 2017, Kaohsiung, Taiwan
|Number of pages
|Published - 18 Oct 2017