Emotion-relevant activity recognition based on smart cushion using multi-sensor fusion

More and more common activities are leading to a sedentary lifestyle forcing us to sit several hours every day. In-seat actions contain significant hidden information, which not only reflects the current physical health status but also can report mental states. Considering this, we design a system, based on body-worn inertial sensors (attached to user's wrists) combined with a pressure detection module (deployed on the seat), to recognise and monitor in-seat activities through sensor- and feature-level fusion techniques. Specifically, we focus on four common basic emotion-relevant activities (i.e. interest-, frustration-, sadness- and happiness-related). Our results show that the proposed method, by fusion of time- and frequency-domain feature sets from all the different deployed sensors, can achieve high accuracy in recognising the considered activities.