ORIGINAL RESEARCH article
Front. Bioeng. Biotechnol.
Sec. Biomechanics
Volume 13 - 2025 | doi: 10.3389/fbioe.2025.1619411
Development of an upper limb muscle strength rehabilitation assessment system using particle swarm optimisation
Provisionally accepted
- 1School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China
- 2City University of Hong Kong, Hong Kong, China
- 3School of Medical Information Engineering, Guangdong Pharmaceutical University, Guang Zhou, China
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Purpose: This study develops a particle swarm optimization (PSO)-based assessment system for evaluating upper extremity and shoulder joint muscle strength with potential application to stroke rehabilitation. This study validates the system on healthy adult volunteers using surface electromyography (sEMG) and joint motion data. Methods: The system comprises a multimodal data acquisition module and a computational analysis pipeline. sEMG signals were collected noninvasively from the anterior, medial, and posterior deltoid muscles using bipolar electrode arrays. These signals are subjected to noise reduction and feature extraction. Simultaneously, triaxial kinematic data of the glenohumeral joint were obtained via an MPU6050 inertial measurement unit, processed through quaternion-based orientation estimation. Machine learning models, including Backpropagation Neural Network (BPNN), Support Vector Machines (SVM), and particle swarm optimization algorithms (PSO-BPNN, PSO-SVR), were applied for regression analysis. Model performance was evaluated using R-squared (R²), Root Mean Square Error(RMSE) , Mean Absolute Error (MAE), and Mean Bias Error (MBE). Results: The system successfully collected electromyographic and kinematic data. PSO-SVR achieved the best predictive performance (R² = 0.8600, RMSE = 0.3122, MAE = 0.2453, MBE = 0.0293), outperforming SVR, PSO-BPNN, and BPNN. Conclusion: The PSO-SVR model demonstrated the highest accuracy , which can better facilitate therapists in conducting muscle strength rehabilitation assessments. Significance:This system enhances quantitative assessment of muscle strength in stroke patients, providing a reliable tool for rehabilitation monitoring and personalized therapy adjustments.
Keywords: Upper limb movement disorders, Surface electromyographic signals, feature extraction, Regression prediction, Feature importance, muscle strength assessment PSO-Optimized Upper Limb Rehabilitation Strength Assessment
Received: 01 May 2025; Accepted: 23 Jun 2025.
Copyright: © 2025 Zhou, Wang, Meiyi, Lai, 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:
Jun Yi, School of Medical Information Engineering, Guangdong Pharmaceutical University, Guang Zhou, China
Dong Cao, School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, 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.