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A Multi-Joint Continuous Motion Estimation Method of Lower Limb Using Least Squares Support Vector Machine and Zeroing Neural Network based on sEMG signals

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Abstract

In this paper, an active motion intention recognition technology in view of least squares support vector machine (LS-SVM) and zeroing neural network (ZNN) is proposed, and the continuous motion angles of knee joint and hip joint are successfully estimated from surface electromyography (sEMG). The processed sEMG signal is subjected to nonlinear mapping to gain the muscle activation which is applied as an input of the prediction model, and the output is the actual motion angle measured by the sensor. Compared with the standard SVM algorithm, the advantage of LS-SVM lies in the alteration of constraint conditions, which transforms the original quadratic programming (QP) problem into solving a set of linear equations. ZNN can be exploited to settle the converted equations and increase the calculation rate. In practice, it is difficult to avoid the interference of measurement noise on the recognition process, so a noise-suppressing zeroing neural network (NSZNN) is established and analyzed. Finally, the corresponding experiments show that the proposed method is able to accurately identify the joint angle, and when considering the noise in the solution process, it can eliminate the effect of noise on the estimation result to a certain extent. The numerical results reveal the method raised in this paper provides valuable reference for lower limb joint movement of patients.

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Authors and Affiliations

  1. Department of Control Engineering, Changchun University of Technology, Changchun, 130012, China

    Zhongbo Sun & Keping Liu

  2. Department of Control, Shanghai MicroPort MedBot (Group) Co., Ltd., Room 101, Block B, 1601 Zhangdong Road, Pilot Free Trade Zone, Shanghai, China

    Xin Zhang

  3. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510006, China

    Tian Shi

  4. School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, 454000, China

    Jing Wang

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  1. Zhongbo Sun
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  2. Xin Zhang
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  3. Keping Liu
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  4. Tian Shi
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  5. Jing Wang
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Correspondence to Jing Wang.

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The work is supported in part by the National Natural Science Foundation of China under grants 61873304, in part by the China Postdoctoral Science Foundation Funded Project under grant 2018M641784 and 2019T120240, in part by the Key Science and Technology Projects of Jilin Province, China, grant nos. 20200201291JC, and also supported by “the Fundamental Research Funds for the Universities of Henan Province” grant nos. NSFRF220431.

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Sun, Z., Zhang, X., Liu, K. et al. A Multi-Joint Continuous Motion Estimation Method of Lower Limb Using Least Squares Support Vector Machine and Zeroing Neural Network based on sEMG signals. Neural Process Lett 55, 2867–2884 (2023). https://doi.org/10.1007/s11063-022-10988-2

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