Leveraging Machine Learning for Signal Processing in Surface Electromyography (sEMG) for Prosthetic Control

In this study, we delve into enhancing prosthetic limb control through surface electromyography (sEMG) via machine learning techniques, addressing challenges like signal selectivity and noise. With the backdrop of exponential growth in computational power and artificial intelligence, we employ convolutional neural networks (CNNs), among other algorithms, for feature extraction and movement classification from sEMG data, showcasing CNNs’ superiority in automating feature extraction despite higher computational requirements. Our innovative approach introduces an estimation method incorporating joint angle measurements to refine hand movement estimation, aiming to overcome the limitations of traditional classification methods. This method marks a significant step towards achieving more nuanced and adaptable prosthetic control. Findings reveal that machine learning enhances the precision and flexibility of prosthetic control systems, with the potential to significantly improve amputee patients’ quality of life by providing more responsive and naturalistic limb control. This study not only contributes to advancing prosthetic control technology but also sets the stage for future research to further optimize prosthetic limb functionality.