Enhancing Myoelectric Prosthetic control: Deep Learning Strategies for Continuous Arm Kinematics Estimation and Cross-Subject Model Transferability from EMG Data

Over the last decade, myoelectric prosthesis control has witnessed considerable advancements, yet there remain significant challenges. Two key constraints have been the limited range of movements and the lack of simultaneous control capabilities. This study aims to address these issues by introducing an LSTM-based approach for the continuous control of critical parameters in prosthetic limbs. Utilizing deep learning models, our method enhances the precision in controlling the elbow angle (θ), as well as the horizontal (X) and vertical (Y) positions of the wrist joint, coupled with the velocity (v). This research not only focuses on the spatial and dynamic aspects of the movement but also emphasizes the transferability of our model across different subjects. We have successfully demonstrated the model's ability to be trained on one subject and applied effectively to another.