Hand Gesture Classification Using Nearest Centroid with Soft-DTW Loss on sEMG Signals

Lingfeng Zhang, Zunian Wan, Yepeng Ding, Junwei Yu, Takefumi Ogawa, Hiroyuki Sato

Abstract: Surface electromyography (sEMG) signals find extensive applications in medicine and bioengineering, particularly in rehabilitation and assistive technologies. Gesture classification using sEMG poses challenges such as noise removal, feature extraction, and personalized classification to accommodate individual variations in human physiology. To address these challenges, we propose an sEMG-based gesture classification method by leveraging the nearest centroid classifier and guiding the generation of centroids generated with Soft-DTW as a loss function. Additionally, we apply denoising techniques to the original sEMG signals, including DC offset removal, bandpass filtering, full-wave rectification, and linear envelope extraction. Additionally, we propose a cubic spline for downsampling. With a 1% downsampling rate, our method achieves 89.1% on average and 90.5% at peak and outperforms the state-of-the-art methods.