A Real-Time Hand Gesture Recognition System for Low-Latency HMI via Transient HD-sEMG and In-Sensor Computing

In real-time human-machine interaction (HMI) applications, hand gesture recognition (HGR) requires high accuracy with low latency. Surface electromyography (sEMG), a physiological electrical signal reflecting muscle activation, is extensively used in HMI. Recently, transient sEMG, generated during the gesture transitions, has been employed in HGR to achieve lower observational latency compared to steady-state sEMG. However, the use of long feature windows (up to 200 ms) still make it less desirable in low-latency HMI. In addition, most studies have relied on remote computing, where remote data processing and large data transfer result in high computation and network latency. In this paper, we proposed a method leveraging transient high density sEMG (HD-sEMG) and in-sensor computing to achieve low-latency HGR. An sEMG contrastive convolution network (sCCN) was proposed for HGR. The mean absolute value and its average integration were used to train the sCCN in a contrastive learning manner. In addition, all signal acquisition, data processing, and pattern recognition processes were deployed within designed sensor for in-sensor computing. Compared to the state-of-the-art study using multi-channel 200-ms transient sEMG, our proposed method achieved a comparable HGR accuracy of 0.963, and a 58% lower observational latency of only 84 ms. In-sensor computing realizes a 4 times lower computation latency of 3 ms, and significantly reduces the network latency to 2 ms. The proposed method offers a promising approach to achieving low-latency HGR without compromising accuracy. This facilitates real-time HMI in biomedical applications such as prostheses, exoskeletons, virtual reality, and video games.