A Novel Framework for Motion-Induced Artifact Cancellation in sEMG: Evaluation on English Premier League and Ninapro Datasets

This article addresses the challenge posed by motion-induced artifact (MIA) in surface electromyography (sEMG) signals, a prevalent issue in professional sports settings due to the movements and collisions of athletes. The shared frequency spectra and nonstationary characteristics of MIA and sEMG, coupled with the unpredictable and impulsive occurrence of MIA, cause substantial challenges to conventional filtering and signal-processing-based denoising methods. This study proposes a framework involving two consecutive models specifically designed to detect MIA zones in the sEMG stream and to denoise MIA. Using two distinct deep learning models for each task proves more effective than using a singular model, enhancing the signal-to-noise ratio (SNR) by 3.12 dB. A bidirectional long short-term memory recurrent neural network (BLSTM RNN)-based approach is proposed for detecting MIA zones, achieving macro F1 scores of 94.8% and 95% for synthetic and real-world datasets, respectively. This study uses the publicly available Ninapro dataset, enriched with synthetic MIA, and a unique dataset collected from English Premier League (EPL) athletes, incorporating real MIA. For the denoising of MIA, a novel convolution block within the U-Net encoder decoder (UED) is introduced, featuring attention-enhanced kernel and channel selection, which achieves an SNR improvement (SNRimp) of 17.20 dB. This approach surpasses the best state-of-the-art model by 7.01 dB and exceeds the average of contemporary models by 12 dB, signifying a substantial advancement in the field.