基于GA-BLS方法的手势识别研究

doi:10.3969/j.issn.1000-1158.2024.01.17

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Abstract To further improve the accuracy and speed of gesture recognition in the field of human-computer interaction, and explore the influence of muscle fatigue on gesture recognition, an improved GA-BLS method was proposed, genetic algorithms (GA) were used to optimize the parameters of the broad learning system (BLS) model, and elastic network regression was used to improve the traditional BLS model. The proposed model was used to analyze the A-mode ultrasound signal and EMG signal under eight kinds of gestures for gesture recognition, and compared with SVM, KNN, RF, LDA and other methods to verify the effectiveness of the research methods. Furthermore, the A-mode ultrasound and EMG in a long period of time were divided into four data segments. It was found that with the increase of muscle fatigue, the accuracy of gesture recognition showed a significant downward trend, and A-mode ultrasound signal had better fatigue resistance than EMG signal.

Key words： gesture recognition physiological signals genetic algorithms broad learning system muscle fatigue elastic network regression

Received: 28 April 2023 Published: 22 January 2024

PACS:	TB99
	TB973

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	Articles by authors
	DU Yihao
	CAO Tianfu
	FAN Qiang
	WANG Xiaoran

Cite this article:

DU Yihao,CAO Tianfu,FAN Qiang, et al. Research on Gesture Recognition Based on GA-BLS[J]. Acta Metrologica Sinica, 2024, 45(1): 121-127.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2024.01.17 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2024/V45/I1/121

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