| Summary: | The surface electromyography (sEMG) signals contain a large amount of physiological information reflecting the body’s motor intention. The use of sEMG signals for gesture recognition has received a lot of attention in both robotics and rehabilitation fields. Most of the current studies on gesture recognition based on sEMG signals obtain discrete gestures by classification, ignoring the continuous natural motion. In this paper, a continuous gesture recognition and force estimation method is proposed based on sEMG signals. To establish a foundation for this approach, sEMG sensors are thoughtfully positioned on the forearm’s surface, guided by considerations of physiological structure and muscular function. The finger curvature is proposed to describe the gesture state, and the gesture changes at different moments can be represented by the set of finger curvatures of different fingers, thus achieving continuous gesture recognition. Muscle force estimation was performed while recognizing gestures under different force partterns. A multi-stream convolutional neural network (MSCNN) is used to model finger curvature with sEMG to achieve gesture recognition, and Long short-term memory (LSTM) is used for muscle force estimation due to it is able to capture the temporal relationship during muscle force generation. The experimental results show that the average Pearson correlation coefficient (CC) and root-meansquare error (RMSE) of gesture recognition are 0.84 and 0.11, respectively, and the coefficient of determination <inline-formula> <tex-math notation="LaTeX">$(R^{2}) $ </tex-math></inline-formula> of muscle force estimation is 0.92. The whole scheme achieves the simultaneous estimation of gesture and muscle force, which can well meet the needs in the field of prosthetic control and rehabilitation assistance.
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