A multi-Kalman filter-based approach for decoding arm kinematics from EMG recordings

Background Remarkable work has been recently introduced to enhance the usage of Electromyography (EMG) signals in operating prosthetic arms. Despite the rapid advancements in this field, providing a reliable, naturalistic myoelectric prosthesis remains a significant challenge. Other challenges include the limited number of allowed movements, lack of simultaneous, continuous control and the high computational power that could be needed for accurate decoding. In this study, we propose an EMG-based multi-Kalman filter approach to decode arm kinematics; specifically, the elbow angle (theta), wrist joint horizontal (X) and vertical (Y) positions in a continuous and simultaneous manner. Results Ten subjects were examined from which we recorded arm kinematics and EMG signals of the biceps, triceps, lateral and anterior deltoid muscles corresponding to a randomized set of movements. The performance of the proposed decoder is assessed using the correlation coefficient (CC) and the normalized root-mean-square error (NRMSE) computed between the actual and the decoded kinematic. Results demonstrate that when training and testing the decoder using same-subject data, an average CC of 0.68 +/- 0.1, 0.67 +/- 0.12 and 0.64 +/- 0.11, and average NRMSE of 0.21 +/- 0.06, 0.18 +/- 0.03 and 0.24 +/- 0.07 were achieved for theta, X, and Y, respectively. When training the decoder using the data of one subject and decoding the data of other subjects, an average CC of 0.61 +/- 0.19, 0.61 +/- 0.16 and 0.48 +/- 0.17, and an average NRMSE of 0.23 +/- 0.07, 0.2 +/- 0.05 and 0.38 +/- 0.15 were achieved for theta, X, and Y, respectively. Conclusions These results suggest the efficacy of the proposed approach and indicates the possibility of obtaining a subject-independent decoder.