A Continuous Estimation Model of Upper Limb Joint Angles by Using Surface Electromyography and Deep Learning Method

The continuous control of rehabilitation robots based on surface electromyography (sEMG) is a natural control strategy that can ensure human safety and ease the discomfort of human-machine coupling. However, current models for estimating movement of the upper limb focus on two dimensions movement, and models of three dimensions movement are too complex. In this paper, a simple-structure temporal information-based model for upper limb motion was proposed. An experiment of the multijoint motion of the upper limb was carried out. We studied the touching motion and the compound task motion of the upper limbs. The touch motor task consists of three tasks, namely, shoulder abduction, shoulder forward bend and finger-nose. The compound tasks include driving and fetching objects. Three-dimensional upper limb movement data and sEMG signals of seven upper limb muscles were recorded from seven healthy subjects. Model training was carried out after data preprocessing and feature extraction. Then, 120 s of upper limb motion data was used to verify the performance of the model proposed in this paper. The estimated accuracy of the model for the touch tasks was 0.9171, and it was 0.8109 for compound tasks. Compared to the multilayer perception (MLP) model, a 13.57% reduction in the root-mean-square error (RSME) was observed. The results show that the model has good accuracy for estimating the angular motion of the upper limb and that it has the potential to be applied for three-dimensional motion control in an upper limb mirror-image therapy rehabilitation robot.