Comparing the efficiency of recurrent neural networks to EMG-based continuous estimation of the elbow angle

This study comprehensively assesses various recurrent neural networks (RNNs) for decoding the elbow angle from electromyogram (EMG) signals, a crucial aspect in myoelectric interfaces. EMG signals from the shoulder girdle and arm were recorded during goal-directed reaching movements, and linear envelopes were continuously mapped to the elbow angle by three RNN architectures: nonlinear autoregressive exogenous (NARX), Elman, and long-term short memory (LSTM). All three approaches effectively captured the complex dynamics of the multi-input to a single-output regression problem. Regarding within-subject variability, the NARX, Elman, and LSTM demonstrated superior accuracy and robustness compared to dynamic feedforward neural networks like time-delay neural networks. Notably, there was no statistically significant distinction among NARX, Elman, and LSTM estimation performances. Elman and LSTM exhibited an advantage in decoding latent information dependencies through their context layers, leading to improved estimation performance in inter-subject variability analysis, particularly with increased training data volume and variability. Furthermore, the LSTM, with its complex architecture capable of learning long-term temporal dependencies, exhibited the highest performance among the considered RNNs. Consequently, selecting the optimal RNN structure is recommended based on the complexity of the data at hand. The RNN-based decoding model holds potential applications in prosthetics, robotic assistants, and exoskeletons, enabling intention detection and real-time assessment of active rehabilitation progress.