A Lightweight Hybrid Encoder-Decoder Framework for Multiple Degree of Freedom Muscle Force Estimation

Motor unit (MU) discharge information extracted via real-time electromyogram (EMG) decomposition shows superiority in dexterous finger motion decoding. However, some critical constraints might limit the performance of multi-degree-of-freedom (DoF) muscle force estimation, including the neglect of the temporal cumulative effects of MU discharges and the rigid MU pool allocation across fingers.

A hybrid encoder-decoder framework integrating EMG decomposition with TFR (Temporal Firing Rate)-Net was proposed for multi-DoF force estimation. The initial encoder involved decomposing EMG into MU spike trains and firing rates, which are subsequently processed through TFR-Net for hierarchical encoding and decoding. This framework uses temporal firing rate to model force accumulation instead of a twitch model, and allocates MU weights dynamically and flexibly in multi-fingers and multi-force level tasks instead of rigid MU pool allocation.

Two baselines (firing rate-based regression method and twitch force model method) were evaluated on 15-min EMG from 10 subjects performing alternating multi-finger isometric extensions. The results showed the proposed method demonstrated superior comprehensive performance with a higher correlation (R ${}^{\mathbf {{2}}}$ : $0.80~\pm ~0.12$ vs. $0.75~\pm ~0.14$ vs. $0.62~\pm ~0.19$ ) and a lower prediction error (root-mean-square error: 6.32% $\pm ~1.89$ % vs. 7.24% $\pm ~1.82$ % vs. 9.96% $\pm ~2.54$ % maximum voluntary contraction) compared with the two comparison methods, and an improved computational efficiency (Flops: 1.350k vs. 408.093k vs. 0.016k) compared with the twitch force model method.

Further development of the proposed method could potentially provide a robust human machine interface for dexterous finger force prediction in realistic applications.