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sEMG-based upper limb motion recognition using improved sparrow search algorithm

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Abstract

The study of motion intention recognition of patients has become one of the key directions in the current research on human-machine coordination control of rehabilitation robots. To improve the accuracy of motion intention recognition and shorten the recognition time, in this work, an improved sparrow search algorithm based on multi-strategy (MSISSA) is designed for improving the prediction performance of the classification algorithm for motion pattern recognition of human upper limbs based on 4-channel sEMG signals. For the poor quality of the initial solution of the population, elite initial solutions are defined by an opposition-based learning strategy to enhance the diversity and traversal of the population. Due to the lack of effective step size control and variation mechanism in the iterative process, a nonlinear exponential decreasing strategy is proposed to balance the global search and local exploitation ability of the algorithm, and a vertical and horizontal crossover strategy is introduced after the individual position update in the population to improve the ability of the algorithm to jump out of the local optimum. The cross-sectional and longitudinal experiments show that the proposed MSISSA algorithm has certain advantages in terms of convergence speed, solution accuracy and robustness, and the classifier optimized based on the MSISSA algorithm has a 2.835% improvement in the accuracy of sEMG signal recognition compared with the original classifier, which is of positive significance for the application in acquiring patient intention for robot-assisted rehabilitation motions.

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Data Availability

The raw/processed data cannot be shared temporarily as the data also forms part of an ongoing study.

Code Availability

The code is available upon request.

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Funding

This research is supported by the National Key Research and Development Program of China (2019YFB1312500), National Natural Science Foundation of China (U1913216), Hebei Provincial Key Research Projects (19211820D), Hebei Provincial Key Research Projects (20371801D).

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Author notes

  1. Hongbo Wang

    Present address: Academy for Engineering & Technology, Fudan University, Handan Road, Shanghai, 200433, Shanghai, China

Authors and Affiliations

  1. School of Mechanical Engineering, Tianjin University, Yaguan Road, Tianjin, 300350, Tianjin, China

    Peng Chen

  2. Parallel Robot and Mechatronic System Laboratory of Hebei Province, Yanshan University, Hebei Street, Qinhuangdao, 066000, Hebei, China

    Hongbo Wang, Jiazheng Du, Yuansheng Ning & Jian Wei

  3. Shanghai Clinical Research Center for Aging and Medicine, Urumqi Road, Shanghai, 200040, Shanghai, China

    Hongbo Wang

  4. College of Mechanical and Equipment Engineering, Hebei University of Engineering, Taiji Road, Handan, 056001, Hebei, China

    Hao Yan

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  1. Peng Chen
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Contributions

Methodology, H.W. and P.C.; Software, J.D. and P.C.; Validation, H.Y.; Formal analysis, J.W.; Investigation, P.C. and J.W.; Writing—Original draft preparation, P.C. and J.D.; Writing—Review and editing, H.W., H.Y. and Y.N.; Project administration, H.W.

Corresponding author

Correspondence to Hongbo Wang.

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Ethics approval and consent to participate

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by Ethics Committee of China Rehabilitation Research Center (protocol code 2020-006-1; date of approval February 25, 2020).

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Informed consent was obtained from all participants to publish this paper.

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The authors declare that they have no conflict of interest.

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Chen, P., Wang, H., Yan, H. et al. sEMG-based upper limb motion recognition using improved sparrow search algorithm. Appl Intell 53, 7677–7696 (2023). https://doi.org/10.1007/s10489-022-03824-6

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  • DOI: https://doi.org/10.1007/s10489-022-03824-6

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