Tactile motion recognition with convolutional neural networks

To satisfy the diversity of tactile patterns during Physical Human Robot Interaction(PHRI), this paper proposes a method to recognize human tactile motion using a spherical handle equipped with tactile sensors. The method first exploits convolutional neural networks as universal feature extractors, and then support vector machines are implemented for classifying the 16 kinds of motion in 4D space. Experimental results show the superiority of our approach against other methods, leading to classification rates over 91.19%.