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Open AccessArticle

Estimation and Correlation Analysis of Lower Limb Joint Angles Based on Surface Electromyography

1
Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou 310018, China
2
Department of Mechanical and Electrical Engineering, Xinjiang Institute of Technology, Akesu 843100, China
3
Informatics Center, George Washington University, Washington, DC 20052, USA
*
Authors to whom correspondence should be addressed.
Electronics 2020, 9(4), 556; https://doi.org/10.3390/electronics9040556
Received: 26 February 2020 / Revised: 20 March 2020 / Accepted: 25 March 2020 / Published: 26 March 2020
(This article belongs to the Section Bioelectronics)
Many people lose their motor function because of spinal cord injury or stroke. This work studies the patient’s continuous movement intention of joint angles based on surface electromyography (sEMG), which will be used for rehabilitation. In this study, we introduced a new sEMG feature extraction method based on wavelet packet decomposition, built a prediction model based on the extreme learning machine (ELM) and analyzed the correlation between sEMG signals and joint angles based on the detrended cross-correlation analysis. Twelve individuals participated in rehabilitation tasks, to test the performance of the proposed method. Five channels of sEMG signals were recorded, and denoised by the empirical mode decomposition. The prediction accuracy of the wavelet packet feature-based ELM prediction model was found to be 96.23% ± 2.36%. The experimental results clearly indicate that the wavelet packet feature and ELM is a better combination to build a prediction model.
Keywords: rehabilitation robots; estimation of continuous joint motion; sEMG signals; wavelet packet decomposition; correlation analysis rehabilitation robots; estimation of continuous joint motion; sEMG signals; wavelet packet decomposition; correlation analysis
MDPI and ACS Style

Wang, J.; Wang, L.; Xi, X.; Miran, S.M.; Xue, A. Estimation and Correlation Analysis of Lower Limb Joint Angles Based on Surface Electromyography. Electronics 2020, 9, 556.

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