Abstract
As the medium of human-computer interaction, it is crucial to correctly and quickly interpret the motion information of surface electromyography (sEMG). Deep learning can recognize a variety of sEMG actions by end-to-end training. However, most of the existing deep learning approaches have complex structures and numerous parameters, which make the network optimization problem difficult to realize. In this paper, a novel PSO-based optimized lightweight convolution neural network (PLCNN) is designed to improve the accuracy and optimize the model with applications in sEMG signal movement recognition. With the purpose of reducing the structural complexity of the deep neural network, the designed convolution neural network model is mainly composed of three convolution layers and two full connection layers. Meanwhile, the particle swarm optimization (PSO) is used to optimize hyperparameters and improve the autoadaptive ability of the designed sEMG pattern recognition model. To further indicate the potential application, three experiments are designed according to the progressive process of body movements with respect to the Ninapro standard data set. Experiment results demonstrate that the proposed PLCNN recognition method is superior to the four other popular classification methods.
Highlights
Human-computer interaction is one of the most popular topics in the field of signal processing
The purpose of this paper is to propose a new hand movement recognition method with applications to the analysis of multichannel surface electromyography (sEMG) signals
lightweight convolutional neural network (LWCNN) model has a significant effect on the spatial processing of the sEMG signal, but the settings of hyperparameters can directly affect the performance of the model
Summary
Human-computer interaction is one of the most popular topics in the field of signal processing. Physiological signals and RGB images are two mainstream pieces of information to capture human activities [1,2,3,4,5]. E recognition of physiological-signals-based body movements is the key to human-computer interaction [6,7,8], due to the problems of the image, such as occlusion by the environment, inability to be distinguished accurately, and difficulty in being segmented. As a physiological signal of the human body, sEMG is a complex electrical signal produced by muscle contraction and relaxation, which carries the movement information of corresponding parts. Erefore, the machine is capable of representing the human movement intention; it is the popular research frontier in the field of human-computer interaction. SEMG pattern recognition consists of the following three parts: signal preprocessing, feature extraction, and feature classification. Feature extraction includes the following four aspects: time domain, frequency domain, time-frequency domain, and nonlinear dynamics; see [12, 13]. e papers [14,15,16,17] adopt different improved methods combining time domain and time-frequency domain to extract representative features of EMG
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