Fatigue analysis of upper limb rehabilitation based on surface electromyography signal and motion capture

目前，上肢运动的疲劳状态监测，一般单纯依赖表面肌电信号（sEMG）对疲劳进行识别和分类，导致结果不稳定，存在一定局限。为此，本文将sEMG信号识别与动作捕捉技术引入到疲劳状态监测过程中，提出了一种融合改进的肌电疲劳阈值算法与生物力学分析的疲劳分析方法。本研究通过右上肢负载屈肘试验，同步采集肱二头肌sEMG信号与上肢动作捕捉数据，并同时运用柏格（Borg）疲劳度主观自觉量表记录受试者疲劳感受。然后，将融合改进的肌电疲劳阈值算法和生物力学分析的疲劳分析方法与平均功率频率（MPF）、谱矩比（SMR）、模糊近似熵（fApEn）、Lempel-Ziv复杂度（LZC）四种单一评价指标疲劳评价方法的试验结果进行对比。试验结果表明，本文方法对总体疲劳状态识别率结果达到98.6%，对轻松、过渡、疲劳三种状态的识别率分别达到97%、100%、99%，较其他方法更有优势。本文研究结果证明，本文方法在上肢运动过程中能够有效预防过度训练引起的二次损伤，对于疲劳监护具有重要意义。.; At present, fatigue state monitoring of upper limb movement generally relies solely on surface electromyographic signal (sEMG) to identify and classify fatigue, resulting in unstable results and certain limitations. This paper introduces the sEMG signal recognition and motion capture technology into the fatigue state monitoring process and proposes a fatigue analysis method combining an improved EMG fatigue threshold algorithm and biomechanical analysis. In this study, the right upper limb load elbow flexion test was used to simultaneously collect the biceps brachii sEMG signal and upper limb motion capture data, and at the same time the Borg Fatigue Subjective and Self-awareness Scale were used to record the fatigue feelings of the subjects. Then, the fatigue analysis method combining the EMG fatigue threshold algorithm and the biomechanical analysis was combined with four single types: mean power frequency (MPF), spectral moments ratio (SMR), fuzzy approximate entropy (fApEn) and Lempel-Ziv complexity (LZC). The test results of the evaluation index fatigue evaluation method were compared. The test results show that the method in this paper has a recognition rate of 98.6% for the overall fatigue state and 97%, 100%, and 99% for the three states of ease, transition and fatigue, which are more advantageous than other methods. The research results of this paper prove that the method in this paper can effectively prevent secondary injury caused by overtraining during upper limb exercises, and is of great significance for fatigue monitoring.