Analyzing User Emotions via Physiology Signals

Recently some studies have shown that the major influential factor of our health is not only physical activities, but the states of our emotion that we experience through our daily life, which continuously build our behavior and affect our physical health significantly. Therefore, emotion recognition draws more and more attention for many 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 user and apply machine learning techniques to recognize emotional states of the user. These states are key steps, toward improving not only the physical health but also emotional intelligence in advanced human-machine interaction. Moreover, we consider three types of emotional states and conduct experiments on real-life scenarios. Experimental results shows that the proposed system has good performance for emotion recognition.