Document Type : Full Research Paper
Authors
1 Assistant Professor, Biomedical Engineering Group, Department of Engineering, Meybod University, Meybod, Iran
2 Instructor, Biomedical Engineering Group, Department of Engineering, Meybod University, Meybod, Iran
3 Ph.D. Student, School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202 USA
4 Professor, Biomedical Engineering Group, Department of Engineering, Hakim Sabzevari University, Sabzevar, Iran
Abstract
In modern prostheses, accurate processing of surface electromyogram (sEMG) signals has a significant effect on optimal muscle control. Although these signals are useful for diagnosing neuromuscular diseases, controlling prosthetic devices and detecting hand movements, non-robustness of EMG signal-based recognition will give rise to various movement disorders. In this paper, we present an optimal approach to classify EMG signals for hand gesture and movement recognition, whose purpose is to be used as an efficient method of diagnosing neuromuscular diseases, determining the type of treatment and physiotherapy. The main assumption of this study is to improve the accuracy of recognition and therefore, we proposed a novel hand gesture and movement recognition model consists of three steps: (1) EMG signal features extraction based on time-frequency domain and fractal dimension features; (2) feature selection by soft ensembling of three procedures in which includes two sample T-tests, entropy and common wrapper feature reduction, and (3) classification based on kernel parameters optimization of SVM classifier by using Gases Brownian Motion Optimization (GBMO) algorithm. Two UC2018 DualMyo and UCI datasets have been considered to evaluate the proposed model. The first dataset is used to classify eight hand gestures and the second dataset is employed for the classification of six types of movement. The experiment results and statistical tests reveal that the designed approach has desirable performance with an average accuracy of above 98% in both datasets. Contrary to similar methods that perform classifications in finite classes with high error rates, the integrated method has satisfactory accuracy, robustness and reliability. Not only the proposed method contributes to the design of prostheses, but also provides effective outcomes for rehabilitation applications and clinical diagnosis processes.
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