Un marco híbrido ligero de codificador-decodificador para la estimación de fuerza muscular de múltiples grados de...

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 (R²: 0.80 ± 0.12 vs. 0.75 ± 0.14 vs. 0.62 ± 0.19) and a lower prediction error (root-mean-square error: 6.32% ± 1.89% vs. 7.24% ± 1.82% vs. 9.96% ± 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.