Abstract
In the field of noncontact human-computer interaction, it is of crucial importance to distinguish different surface electromyography (sEMG) gestures accurately for intelligent prosthetic control. Gesture recognition based on low sampling frequency sEMG signal can extend the application of wearable low-cost EMG sensor (for example, MYO bracelet) in motion control. In this paper, a combination of sEMG gesture recognition consisting of feature extraction, genetic algorithm (GA), and support vector machine (SVM) model is proposed. Particularly, a novel adaptive mutation particle swarm optimization (AMPSO) algorithm is proposed to optimize the parameters of SVM; moreover, a new calculation method of mutation probability is also defined. The AMPSO-SVM model based on combination processing is successfully applied to MYO bracelet dataset, and four gesture classifications are carried out. Furthermore, AMPSO-SVM is compared with PSO-SVM, GS-SVM, and BP. The sEMG gesture recognition rate of AMPSO-SVM is 0.975, PSO-SVM is 0.9463, GS-SVM is 0.9093, and BP is 0.9019. The experimental results show that AMPSO-SVM is effective for low-frequency sEMG signals of different gestures.
Highlights
Surface electromyography signal is the superposition of action potentials of multiple active motor units in time and space during muscle contraction
Hand gesture recognition based on surface electromyography (sEMG) has been widely used in the diagnosis of skeletal muscle system diseases, rehabilitation medicine, biological feedback, and human-computer interaction, especially in the field of sEMG prosthetic control [2,3,4]. ere are many methods and researches on gesture recognition based on sEMG which mainly focus on signal feature extraction, feature selection, and classification [5]
According to the above three evaluation indicators, it can be seen from Figure 7 that the multiclassification performance of particle swarm optimization (PSO)-support vector machine (SVM) algorithm is slightly better than GSSVM algorithm and BP algorithm, while adaptive mutation particle swarm optimization (AMPSO)-SVM algorithm is obviously the best of all algorithms
Summary
Surface electromyography signal is the superposition of action potentials of multiple active motor units in time and space during muscle contraction. MYO bracelet is a noninvasive surface electromyography acquisition method developed by almic laboratory It provides a more convenient interface for motion control based on human gesture recognition. (1) A novel adaptive mutation method is proposed to improve the original PSO algorithm; a new calculation method of mutation probability is introduced in this paper (2) A combined gesture recognition method based on a low sampling rate EMG signal is proposed, which has the advantages of simple calculation, fast acquisition speed, and high classification accuracy (3) A feature selection strategy, which can effectively remove the redundancy between features and improve the accuracy of subsequent sEMG classification algorithms, is designed to solve the problem of high feature dimension e rest of this paper is arranged as follows.
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