Subject-independent Estimation of Continuous Movements using CNN-LSTM for a Home-based Upper Limb Rehabilitation System

Exoskeleton-assisted home-based rehabilitation plays a vital role in the upper limb rehabilitation of stroke patients in early stage. The surface electromyography (sEMG)-based control can facilitate friendly interactions between individuals and rehabilitation exoskeletons. The exoskeleton can also meet the requirements of home-based rehabilitation, including affordability, portability, safety, and active participation. Although various systems have been proposed to enhance upper limb training, few studies have addressed the inter-subject variability of sEMG signals, which limits the generalization capability of the intention estimation model. In this paper, a subject-independent continuous motion estimation method combining convolutional neural networks (CNN) and long and short-term memory (LSTM) is proposed and applied to a home-based bilateral training system. The sEMG-driven CNN-LSTM model builds the relationship between sEMG signals and continuous movements. To verify the effectiveness of the CNN-LSTM model in achieving subject-independent estimation, the offline estimation under the backpropagation neural network, CNN, and CNN-LSTM are compared. Moreover, the online intention estimation and the real-time control are performed, and the estimation angle error and time delay are controlled at approximately 10°and 300 ms, proving the feasibility of the subject-independent estimation method and its availability in the upper-limb rehabilitation system.