Improving movement decoding performance under joint constraints based on a neural-driven musculoskeletal model

Electromyography-driven musculoskeletal model (E-DMM) connects the user's control commands with the joint positions from a physiological perspective. However, features extracted directly from the surface EMG signals may be affected by signal crosstalk and amplitude cancellation. This limitation can be addressed with the decomposition algorithms for high-density (HD) EMG signals, which demonstrate the capability of extracting neural drives for the human-machine interface. On this basis, we proposed a neural-driven musculoskeletal model (N-DMM) with improved movement decoding performance for estimating wrist and metacarpophalangeal (MCP) joint positions under joint constraints. Eight limb-intact subjects participated in the experiment of mirrored bilateral training. The wrist and MCP joints of the subjects on one side were constrained, and the HD EMG signals from the same side were recorded. Moreover, the unconstrained side mirrored the joint movements of the phantom limb, while the joint angles were measured simultaneously. The obtained EMG signals were processed with the fast independent component analysis algorithm to extract motor unit discharges, enabling the estimation of neural drives. Then the neural drives were taken as inputs for the N-DMM to estimate joint movements. For comparison, an E-DMM was also employed for joint angle prediction. The results indicated that our N-DMM demonstrated superior performance compared to the E-DMM, potentially allowing for more accurate and robust decoding of continuous movements under joint constraints. Further improvement of the proposed model could offer a promising approach for practical applications in amputees.