IEEE Trans Neural Syst Rehabil Eng. 2024;32:1725-1734. doi: 10.1109/TNSRE.2024.3393132. Epub 2024 May 1.
Muscle fatigue significantly impacts coordination, stability, and speed in daily activities. Accurate assessment of muscle fatigue is vital for effective exercise programs, injury prevention, and sports performance enhancement. Current methods mostly focus on individual muscles and strength evaluation, overlooking overall fatigue in multi-muscle movements. This study introduces a comprehensive muscle fatigue model using non-negative matrix factorization (NMF) weighting. NMF is employed to analyze the duration multi-muscle weight coefficient matrix (DMWCM) during synergistic movements, and four electromyographic (EMG) signal features in time, frequency, and complexity domains are selected. Particle Swarm Optimization (PSO) optimizes feature weights. The DMWCM and weighted features combine to calculate the Comprehensive Muscle Fatigue Index (CMFI) for multi-muscle synergistic movements. Experimental results show that CMFI correlates with perceived exertion (RPE) and Speed Dynamic Score (SDS), confirming its accuracy and real-time tracking in assessing multi-muscle synergistic movements. This model offers a more comprehensive approach to muscle fatigue assessment, with potential benefits for exercise training, injury prevention, and sports medicine.
肌肉疲劳会显著影响日常活动中的协调性、稳定性和速度。准确评估肌肉疲劳对于有效的运动计划、预防损伤和提高运动表现至关重要。目前的方法主要侧重于单个肌肉和力量评估,而忽略了多肌肉运动中的整体疲劳。本研究提出了一种使用非负矩阵分解(NMF)权重的综合肌肉疲劳模型。NMF 用于分析协同运动中的多肌肉权重系数矩阵(DMWCM),并选择四个肌电图(EMG)信号在时间、频率和复杂度域中的特征。粒子群优化(PSO)优化特征权重。DMWCM 和加权特征结合起来计算多肌肉协同运动的综合肌肉疲劳指数(CMFI)。实验结果表明,CMFI 与感知用力(RPE)和速度动态评分(SDS)相关,证实了其在评估多肌肉协同运动中的准确性和实时跟踪能力。该模型提供了一种更全面的肌肉疲劳评估方法,对于运动训练、预防损伤和运动医学具有潜在的益处。