SEMG-Driven Functional Electrical Stimulation Tuning via Muscle Force

Accurate force control plays a crucial role but still faces many challenges in functional electrical stimulation (FES) based neuroprosthesis applications. This study proposes an surface electromyography (sEMG) driven solution for FES sequential parameter estimation including its amplitude and pulsewidth (PW). The algorithm forms the mapping relationship from voluntary sEMG to the two FES parameters via the correlated muscle forces as an intermediate variable. Six able-bodied subjects are recruited to optimize and validate the proposed method based on the triangular and random grip force. The estimated amplitude and PW are evaluated respectively by stimulating the ipsilateral and contralateral muscle of the sEMG recording points. The accuracy of muscle force reconstruction achieve by the method are investigated by comparing the FES-induced grip force and the originally recorded voluntary grip force (VGF), in terms of the correlation index (R). The experimental results reveals excellent performance of the method on inducing functional muscle contraction closely matched the VGF (R>0.9), regardless of the estimated amplitudes or PW. Furthermore, the performance differences between the estimated PW and amplitude are not significant (p>0.05). These results not only demonstrate the feasibility of the proposed proportional FES control method, but also confirms its practical potential for muscle force control and strengthening in neuroprosthesis applications.