College of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, China.
College of Coal Engineering, Shanxi Datong University, Datong 037009, China.
Sensors (Basel). 2023 Nov 9;23(22):9055. doi: 10.3390/s23229055.
Fatigue of miners is caused by intensive workloads, long working hours, and shift-work schedules. It is one of the major factors increasing the risk of safety problems and work mistakes. Examining the detection of miner fatigue is important because it can potentially prevent work accidents and improve working efficiency in underground coal mines. Many previous studies have introduced feature-based machine-learning methods to estimate miner fatigue. This work proposes a method that uses electroencephalogram (EEG) signals to generate topographic maps containing frequency and spatial information. It utilizes a convolutional neural network (CNN) to classify the normal state, critical state, and fatigue state of miners. The topographic maps are generated from the EEG signals and contrasted using power spectral density (PSD) and relative power spectral density (RPSD). These two feature extraction methods were applied to feature recognition and four representative deep-learning methods. The results showthat RPSD achieves better performance than PSD in classification accuracy with all deep-learning methods. The CNN achieved superior results to the other deep-learning methods, with an accuracy of 94.5%, precision of 97.0%, sensitivity of 94.8%, and F1 score of 96.3%. Our results also show that the RPSD-CNN method outperforms the current state of the art. Thus, this method might be a useful and effective miner fatigue detection tool for coal companies in the near future.
矿工疲劳是由高强度的工作量、长时间工作和轮班工作时间表引起的。它是增加安全问题和工作失误风险的主要因素之一。检查矿工疲劳的检测很重要，因为它可以潜在地防止工作事故，并提高地下煤矿的工作效率。许多先前的研究已经介绍了基于特征的机器学习方法来估计矿工的疲劳程度。这项工作提出了一种利用脑电图（EEG）信号生成包含频率和空间信息的地形图的方法。它利用卷积神经网络（CNN）对矿工的正常状态、临界状态和疲劳状态进行分类。地形图是从 EEG 信号生成的，并使用功率谱密度（PSD）和相对功率谱密度（RPSD）进行对比。这两种特征提取方法用于特征识别和四种代表性的深度学习方法。结果表明，在所有深度学习方法中，RPSD 在分类准确性方面的性能优于 PSD。CNN 在所有深度学习方法中取得了优于其他方法的结果，准确率为 94.5%，精度为 97.0%，灵敏度为 94.8%，F1 得分为 96.3%。我们的结果还表明，RPSD-CNN 方法优于当前的技术水平。因此，这种方法可能是未来煤炭公司矿工疲劳检测的一种有用且有效的工具。
