Physical fatigue is one of the most important and highly prevalent occupational hazards in different industries. This research adopts a new analytical framework to detect workers’ physical fatigue using heart rate measurements. First, desired features are extracted from the heart signals using different entropies and statistical measures. Then, a feature selection method is used to rank features according to their role in classification. Finally, using some of the frequently used classification algorithms, physical fatigue is detected. The experimental results show that the proposed method has excellent performance in recognizing the physical fatigue. The achieved accuracy, sensitivity, and specificity rates for fatigue detection are 90.36%, 82.26%, and 96.2%, respectively. The proposed method provides an efficient tool for accurate and real-time monitoring of physical fatigue and aids to enhance workers’ safety and prevent accidents. It can be useful to develop warning systems against high levels of physical fatigue and design better resting times to improve workers’ safety. This research ultimately aids to improve social sustainability through minimizing work accidents and injuries arising from fatigue.

中文翻译：

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使用心率信号的熵分析进行身体疲劳检测

身体疲劳是不同行业中最重要和最普遍的职业危害之一。这项研究采用了一种新的分析框架，通过心率测量来检测工人的身体疲劳。首先，使用不同的熵和统计量度从心脏信号中提取所需特征。然后，使用特征选择方法根据特征在分类中的作用对特征进行排序。最后，使用一些常用的分类算法，检测身体疲劳。实验结果表明，所提出的方法在识别身体疲劳方面具有优异的性能。疲劳检测的准确率、灵敏度和特异性分别为 90.36%、82.26% 和 96.2%。所提出的方法为准确和实时监测身体疲劳提供了一种有效的工具，并有助于提高工人的安全和预防事故。开发针对高度身体疲劳的警告系统并设计更好的休息时间以提高工人的安全可能会很有用。这项研究最终有助于通过最大限度地减少因疲劳引起的工作事故和伤害来提高社会可持续性。