MS-ALNET: A Multi-Scale Attention-Based Fusion Framework for Continuous Lower-Limb Movement Prediction Using sEMG and A-Mode Ultrasound

Abstract

Accurate estimation of lower limb motion intention is very important for adaptive control of wearable robot. To address the limited robustness of single sensor mode, this paper proposes a motion prediction framework based on the fusion of surface electromyography (sEMG) and A-mode ultrasound (AUS) signals. The framework uses MS-ALNet deep learning model to process neural driving signals and muscle morphological features through multi-scale feature extraction and attention mechanism. In the walking experiment with 8 subjects, the prediction effects of different signal combinations and mainstream deep learning models were compared. The experimental results show that the prediction accuracy of sEMG+AUS fusion mode is significantly better than that of single mode in hip, knee and ankle joints. The proposed MS-ALNet model is outperforms transformer and CNN-LSTM models in terms of $\mathrm{R}^{2}$ score (both greater than $0.96)$ and RMSE (ankle joint as low as $1.61 \pm 0.2$ °). These results suggest that MS-ALNet can effectively use the complementarity of multimodal signals to improve motion intention estimation under the tested conditions. It provides a potential sensing scheme for rehabilitation robots.