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

This paper 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 non-stationary 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. Employing 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 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 utilizes 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 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.

Recommended citation: https://ieeexplore.ieee.org/document/10542637