Surface electromyography (sEMG) is a non-invasive technique to assess the electrical activity of contracting skeletal muscles. sEMG-based muscle fatigue detection plays a key role in sports medicine, ergonomics and rehabilitation. These signals are random, multicomponent, nonlinear and the degree of fluctuations is higher in dynamic contractions. Hence, the extraction of reliable biomarkers remains a challenging task. In this work, an attempt has been made to differentiate non-fatigue, and fatigue conditions using nonlinear techniques, namely, binary and weighted Visibility Graph (VG) features. For this, signals are recorded from the biceps brachii muscle of 52 healthy adult volunteers. These signals are preprocessed, and the contractions associated with the non-fatigue and fatigue conditions are segmented. The graph transformation is performed, and first-order and second-order statistics, along with entropy measures, are extracted from the degree distribution. Parametric and non-parametric machine learning methods are applied for the classification. The results show that the proposed VG approach is able to capture the fluctuations of the signals in non-fatigue and fatigue conditions. Further, all extracted features exhibit a significant difference with p <0.05. Maximum accuracy of 89.1% is achieved with information gain selected features and extreme learning machines classifier. Additionally, weighted VG features perform better than the binary version with a difference in the accuracy of 5%. It appears that the proposed approach could be used in real-time implementation for the monitoring of muscle fatigue conditions.

中文翻译：

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使用二进制和加权可见性图特征的基于表面肌电图的肌肉疲劳分析

表面肌电图 (sEMG) 是一种评估收缩骨骼肌电活动的非侵入性技术。基于 sEMG 的肌肉疲劳检测在运动医学、人体工程学和康复方面发挥着关键作用。这些信号是随机的、多分量的、非线性的，并且在动态收缩中波动程度更高。因此，可靠的生物标志物的提取仍然是一项具有挑战性的任务。在这项工作中，尝试使用非线性技术（即二进制和加权可见性图 (VG) 特征）来区分非疲劳和疲劳条件。为此，从 52 名健康成年志愿者的肱二头肌记录信号。这些信号经过预处理，与非疲劳和疲劳条件相关的收缩被分割。执行图形转换，从度分布中提取一阶和二阶统计量以及熵度量。参数和非参数机器学习方法用于分类。结果表明，所提出的 VG 方法能够捕捉非疲劳和疲劳条件下的信号波动。此外，所有提取的特征与p <0.05. 使用信息增益选择特征和极限学习机分类器实现了 89.1% 的最大准确度。此外，加权 VG 特征的性能优于二进制版本，精度差异为 5%。看来，所提出的方法可用于实时监测肌肉疲劳状况。