Continuous estimation of upper limb joint angle from sEMG signals based on SCA-LSTM deep learning approach

Robotic arm control has drawn a lot of attention along with the development of industrialization. The methods based on myoelectric pattern recognition have been proposed with multiple degrees of freedom for years. While these methods can support the actuation of several classes of discrete movements sequentially, they do not allow simultaneous control of multiple movements in a continuous manner like natural arms. In this study, we proposed a short connected autoencoder long short-term memory (SCA-LSTM) based simultaneous and proportional (SP) scheme that estimates continuous arm movements using kinematic information extracted from surface electromyogram (sEMG) recordings. The sEMG signals corresponding to seven classes of shoulder-elbow joint angle movements acquired from eleven participants were preprocessed using max root mean square envelope. Afterwards, the proposed SCA-LSTM model and two commonly applied models, namely, multilayer perceptrons (MLPs) and convolutional neural network (CNN), were trained and tested using the preprocessed data for continuous estimation of arm movements. Our experimental results showed that the proposed SCA-LSTM model could achieve a significantly higher estimation accuracy of approximately 95.7% that is consistently stable across the subjects in comparison to the CNN (86.8%) and MLP (83.4%) models. These results suggest that the proposed SCA-LSTM would be a promising model for continuous estimation of upper limb movements from sEMG signals for prosthetic control.