Complicacy in Electrode Position Shift and Its Solution in sEMG Pattern Recognition: A Review

Upper limb amputation can severely restrict an individual’s ability to perform daily tasks. Myoelectric prostheses aim to restore the functionality of amputated limbs by utilizing signals from the remaining muscles in the residual limb. However, achieving high pattern recognition accuracy from acquired surface electromyography (sEMG) signals is a complex challenge. Several significant factors—including electrode position shifts during donning and doffing, inter-user variability, and issues related to muscle sweating—pose obstacles to attaining an accuracy rate exceeding 90%. These challenges can result in the production of prosthetic hands that do not function as intended. To address these issues, modified machine learning schemes and deep learning algorithms can be implemented to find optimal solutions. This review serves as a valuable resource for researchers, helping them understand the challenges.