Static Force Prediction: Comparative Analysis and Fusion Strategies Between Surface Electromyography and Electrical...

Force prediction is crucial for functional rehabilitation of the upper limb. Surface electromyography (sEMG) signals play a pivotal role in muscle force studies, but its non-stationarity challenges the reliability of sEMG-driven models. This problem may be alleviated by fusion with electrical impedance myography (EIM), an active sensing technique incorporating tissue morphology information. This study designed a wearable multimodal physiological measurement system to acquire sEMG and EIM signals simultaneously. The feature quantification indexes were defined for quantitative analysis of the efficacy of EIM and sEMG in static force prediction. We finally proposed Self-Attention Convolutional Long Short-Term Memory (SACLSTM) network to capture the spatio-temporal information among EIM and sEMG features for cross-modal feature fusion. The results indicated that EIM exhibited greater sensitivity to variations in static force compared to sEMG, especially at low muscle activation levels. Furthermore, the proposed SACLSTM network is significantly superior to LSTM, ConvLSTM, and several other baseline methods. Compared to the LSTM and ConvLSTM networks, the SACLSTM model exhibits an ${R}^{{2}}$ improvement of 12.4% and 3%, respectively, and an root mean square error reduction of 63% and 29%. Especially for patients with upper limb dysfunction, the accuracy and stability of the multimodal model were significantly improved after feature fusion compared with using only EIM or sEMG unimodal features. This study emphasised the great potential of fusing EIM and sEMG features to improve performance in the muscle force prediction, opening up new practice paths in the field of functional motor rehabilitation.