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Hand medical monitoring system based on machine learning and optimal EMG feature set

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Abstract

Considering that serious hand function damage will greatly affect the daily life of patients, its recovery mainly depends on the regular inspection and manual training of medical staff, and medical monitoring based on bioelectric signals can largely replace manual re-examination as autonomous rehabilitation technology. So, for the rationality of feature selection and the diversity of classifier design in the gesture recognition process based on electromyography (EMG) signals, this paper proposes a hand medical monitoring system based on feature selection method of feature subset average recognition rate and optimal machine learning algorithm selection, which mainly depends on the prediction of hand movement. At the same time, since most experiments are conducted in different non-public proprietary databases, the comparison between various gesture recognition methods can only be analyzed to a certain extent. Therefore, this paper uses the DB1 dataset in the large publicly available NinaPro database and combines with presently well-known 11 time-domain (TD) features and 5 frequency domain (FD) features, then uses the support vector machine (SVM) classifier to comparative analysis total 136 feature combinations under various feature numbers. Under the premise of ensuring the overall recognition rate of electromyography gesture, this method will be able to reduce the number of features in feature set, according to the change of the average remove redundant features, and construct an optimal reduced EMG feature set. Finally, through the four common hand motion classifiers based on machine learning: SVM, back propagation neural network, linear discriminant analysis, and K-nearest neighbor, this paper tests and verifies the separability of the optimal reduced EMG feature set, and based on this, selects the optimal hand motion classifier to build the optimal hand motion recognition system, improve the hand medical monitoring system, and provide technical reference for the construction of real-time medical monitoring system.

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Funding

This work was supported by grants from the National Natural Science Foundation of China (Grant Nos. 51575407, 51575338, 51575412, 51505349, and 61733011), the grants of National Defense Pre-Research Foundation of Wuhan University of Science and Technology (GF201705), and the open fund of The Key Laboratory for Metallurgical Equipment and Control of Ministry of Education in Wuhan University of Science and Technology (Grant No. 2018B07).

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Authors and Affiliations

  1. Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, China

    Mingchao Yu, Gongfa Li, Du Jiang & Bo Tao

  2. Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, 430081, China

    Gongfa Li

  3. Research Center of Biologic Manipulator and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, 430081, China

    Gongfa Li & Bo Tao

  4. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China

    Guozhang Jiang

  5. 3D Printing and Intelligent Manufacturing Engineering Institute, Wuhan University of Science and Technology, Wuhan, 430081, China

    Guozhang Jiang

  6. School of Computing, University of Portsmouth, Portsmouth, PO1 3HE, UK

    Disi Chen

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  1. Mingchao Yu
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  2. Gongfa Li
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  3. Du Jiang
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Correspondence to Gongfa Li.

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Yu, M., Li, G., Jiang, D. et al. Hand medical monitoring system based on machine learning and optimal EMG feature set. Pers Ubiquit Comput 27, 1991–2007 (2023). https://doi.org/10.1007/s00779-019-01285-2

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