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ORIGINAL RESEARCH article

Front. Neurol.
Sec. Neurorehabilitation
Volume 15 - 2024 | doi: 10.3389/fneur.2024.1470759
This article is part of the Research Topic New approaches for central nervous system rehabilitation - Volume II View all articles

Design of Upper Limb Muscle Strength Assessment System Based on Surface Electromyography Signals and Joint Motion

Provisionally accepted
Siqi Wang Siqi Wang 1Wei Lai Wei Lai 1Yipeng Zhang Yipeng Zhang 1Junyu Yao Junyu Yao 1Xingyue Gou Xingyue Gou 1*Hui Ye Hui Ye 1*Jun Yi Jun Yi 2*Dong Cao Dong Cao 1*
  • 1 School of medical information engineering, Guangzhou University of Chinese Medicine, Guangdong, China
  • 2 School of medical information engineering, Guangdong Pharmaceutical University, Guangzhou, Guangdong Province, China

The final, formatted version of the article will be published soon.

    Purpose: This study aims to develop a assessment system for evaluating shoulder joint muscle strength in patients with varying degrees of upper limb injuries post-stroke, using surface electromyographic (sEMG) signals and joint motion data. Methods: The assessment system includes modules for acquiring muscle electromyography (EMG) signals and joint motion data.The EMG signals from the anterior, middle, and posterior deltoid muscles were collected, filtered, and denoised to extract time-domain features. Concurrently, shoulder joint motion data were captured using the MPU6050 sensor and processed for feature extraction. The extracted features from the sEMG and joint motion data were analyzed using three algorithms: Random Forest (RF), Backpropagation Neural Network (BPNN), and Support Vector Machines (SVM), to predict muscle strength through regression models. Model performance was evaluated using Root Mean Squared Error (RM SE), R-Square (R 2 ), Mean Absolute Error (M AE), and Mean Bias Error (M BE), to identify the most accurate regression prediction algorithm. Results:The system effectively collected and analyzed the sEMG from the deltoid muscles and shoulder joint motion data. Among the models tested, the Support Vector Regression (SVR) model achieved the highest accuracy with an R 2 of 0.8059, RM SE of 0.2873, M AE of 0.2155, and M BE of 0.0071.The Random Forest model achieved an R 2 of 0.7997, RM SE of 0.3039, M AE of 0.2405, and M BE of 0.0090. The BPNN model achieved an R 2 of 0.7542, RM SE of 0.3173, M AE of 0.2306, and M BE of 0.0783. Conclusion: The SVR model demonstrated superior accuracy in predicting muscle strength. The RF model, with its feature importance capabilities, provides valuable insights that can assist therapists in the muscle strength assessment process.

    Keywords: Upper limb movement disorders, Surface electromyographic signals, feature extraction, Regression prediction, Feature importance, Muscle strength assessment

    Received: 01 Aug 2024; Accepted: 27 Nov 2024.

    Copyright: © 2024 Wang, Lai, Zhang, Yao, Gou, Ye, Yi and Cao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Xingyue Gou, School of medical information engineering, Guangzhou University of Chinese Medicine, Guangdong, China
    Hui Ye, School of medical information engineering, Guangzhou University of Chinese Medicine, Guangdong, China
    Jun Yi, School of medical information engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, Guangdong Province, China
    Dong Cao, School of medical information engineering, Guangzhou University of Chinese Medicine, Guangdong, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.