Touch Gesture Recognition Using Spatiotemporal Fusion Features

Abstract

The touch gesture is one of the most essential and effective means to transfer affective feelings and intents in humans’ communication. For an intelligent agent or a robot, the ability to automatically detect and recognize human touch can realize efficient and natural human–robot interaction. To this end, a novel spatiotemporal fusion feature extraction method is proposed for touch gesture classification tasks. The proposed method extracts time-frequency features from wavelet coefficients based on discrete wavelet transforms. Then, the feature array of space and frequency bands is constructed to extract the spatiotemporal fusion features. A publicly available touch gesture dataset called CoST is used to perform the touch gesture recognition. The recognition result of 14 gesture classes using a user-independent model yields an accuracy of up to 64.17%. Experimental results show that this method outperforms the state-of-the-art ones and that the spatiotemporal fusion features effectively boost the performance of touch gesture recognition.