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| Surface electromyography gesture recognition based on personalized feature analysis |
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Abstract To realize artificial limb control, it is an important method to use surface electromyography (sEMG) signal to recognize gestures. sEMG signals are electromyographic signals collected from the surface layer of the skin, which can reflect the neuromuscular activities to a certain extent. Due to its random and non-smooth characteristics, the use of sEMG signals to control artificial intelligent prosthetic limbs has never been able to achieve the desired results, especially reflected in the influence of individual differences such as gender. In this paper, a gesture recognition method based on surface EMG signals is proposed for seven types of gesture movements through differentiated gender characterization. Experimental results show that the recognition accuracy of the method is significantly improved in all five classifiers. Among them, linear discriminant analysis has the most significant improvement, reaching 15.84%; support vector machine and extreme gradient boosting are the most effective in gesture recognition, with an accuracy of 98.41%.
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Received: 26 April 2024
Published: 18 December 2024
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2024, Vol. 45 
