Abstract
The deep learning gesture recognition based on surface electromyography plays an increasingly important role in human-computer interaction. In order to ensure the high accuracy of deep learning in multistate muscle action recognition and ensure that the training model can be applied in the embedded chip with small storage space, this paper presents a feature model construction and optimization method based on multichannel sEMG amplification unit. The feature model is established by using multidimensional sequential sEMG images by combining convolutional neural network and long-term memory network to solve the problem of multistate sEMG signal recognition. The experimental results show that under the same network structure, the sEMG signal with fast Fourier transform and root mean square as feature data processing has a good recognition rate, and the recognition accuracy of complex gestures is 91.40%, with the size of 1 MB. The model can still control the artificial hand accurately when the model is small and the precision is high.
Highlights
IntroductionSurface electromyography (sEMG) has received great attention in driving prosthetic hand [1]
In recent years, surface electromyography has received great attention in driving prosthetic hand [1]
In order to realize the motor control using surface electromyography (sEMG) accurately, gesture action classification based on machine learning (ML) method, namely, pattern recognition (PR) and regression method based on classifier, has been widely studied
Summary
Surface electromyography (sEMG) has received great attention in driving prosthetic hand [1]. In order to realize the motor control using sEMG accurately, gesture action classification based on machine learning (ML) method, namely, pattern recognition (PR) and regression method based on classifier, has been widely studied. The regression model is mainly used for continuous wrist motion estimation [2], which can be used for synchronous control of multidegree of freedom (DOF), while the PRbased method uses discrete and sequential methods to distinguish gesture actions. Some ML-based regression methods, including linear regression (LR), random forest (RF), support vector regression (SVR), and artificial neural network (ANN), have been widely used in offline simulation and real-time control [3,4,5,6,7]. The appearance of convolutional neural network (CNN) provides a new method for feature learning and extraction through layer by layer processing [9, 10]
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