Dual Transformer Network for Predicting Joint Angles and Torques From Multi-Channel EMG Signals in the Lower Limbs.

Abstract

Accurate estimation of lower limb joint kinematics and kinetics using wearable sensors enables biomechanical analysis beyond laboratory settings and facilitates real-time adaptation of exoskeleton assistance profiles. This study introduces a Dual Transformer Network (DTN) designed to concurrently estimate multiple joint angles and moments from multi-channel surface electromyography (sEMG) signals in the lower limbs. The performance evaluation of the predicted joint angles for the hip, knee, and ankle showed average root mean square error (RMSE) values of 1.1827, 1.4312, and 0.8113, Pearson correlation coefficients () of 0.9992, 0.9993, and 0.9991, and coefficients of determination () of 0.9847, 0.9858, and 0.9838, respectively. For the predicted joint moments, the corresponding values were RMSE of 0.0458, 0.0341, and 0.0522 Nm/kg, of 0.9978, 0.9972, and 0.9990, and of 0.9825, 0.9801, and 0.9902. Angular velocities, derived by differentiating the estimated joint angles, achieved an RMSE below 0.6530 rd/s, exceeding 0.9534, and above 0.9552. Additionally, joint power, computed as the dot product of predicted joint moments and angular velocities, resulted in RMSE below 0.3823W/kg, above 0.9771, and above 0.8925. These results demonstrate the effectiveness of the proposed network in continuously estimating lower limb kinematics and kinetics, contributing to advancements in assist-as-needed exoskeleton control strategies.