Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China.
Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China.
Comput Methods Programs Biomed. 2021 Nov;211:106451. doi: 10.1016/j.cmpb.2021.106451. Epub 2021 Oct 2.
Human factors are important contributors to accidents, especially human error induced by fatigue. In this study, field tests and analyses were conducted on physiological indexes extracted from electrocardiography (ECG) and electromyography (EMG) signals in miners working under the extreme conditions of a plateau environment. To provide insights into models for fatigue classification and recognition based on machine learning, multi-modal feature information fusion and miner fatigue identification based on ECG and EMG signals as physiological indicators were studied.
Fifty-five miners were randomly selected as field test subjects, and characteristic signals were extracted from 110 groups of ECG and EMG signals as the basic signals for fatigue analysis. We conducted principal component analysis (PCA) and grey relational analysis (GRA) on the measurement indicators. Support vector machine (SVM), random forest (RF) and extreme gradient boosting (XG-Boost) machine learning models were used for fatigue classification based on multi-modal information fusion. The area under the receiver operating characteristic (ROC) curve and the confusion matrix were used to evaluate the performance of the recognition models.
The ECG and EMG signals showed obvious changes with fatigue. The results of fatigue model identification showed that PCA feature fusion was superior to GRA feature fusion for all three machine learning approaches, and XG-Boost achieved the best performance, with a recognition accuracy of 89.47%, a sensitivity and specificity of 100%, and an AUC of 0.90. The SVM model also showed good recognition performance (89.47% accuracy, AUC=0.89). The worst performance was that of the RF model, with a recognition accuracy of only 78.95%.
This study shows that the physiological indexes of ECG and EMG exhibit obvious, regular changes with fatigue and that it is feasible to use SVM, RF and XG-Boost models for miner fatigue identification. The PCA fusion technique can improve the identification accuracy more than the GRA method. XG-Boost classification yields the best accuracy and robustness. This study can serve as a reference for clinical research on the identification of human fatigue at high altitudes and for the clinical study of acute mountain sickness and human acclimatization to high altitudes.
人为因素是导致事故的重要因素，尤其是由疲劳引起的人为失误。本研究对高原极端环境下矿工的心电图（ECG）和肌电图（EMG）信号中提取的生理指标进行了现场测试和分析。为了为基于机器学习的疲劳分类和识别模型提供参考，研究了基于多模态特征信息融合和矿工疲劳识别的 ECG 和 EMG 信号生理指标。
随机选取 55 名矿工作为现场测试对象，从 110 组 ECG 和 EMG 信号中提取特征信号作为疲劳分析的基本信号。对测量指标进行主成分分析（PCA）和灰色关联分析（GRA）。基于多模态信息融合，使用支持向量机（SVM）、随机森林（RF）和极端梯度提升（XG-Boost）机器学习模型进行疲劳分类。采用受试者工作特征（ROC）曲线下面积和混淆矩阵评估识别模型的性能。
ECG 和 EMG 信号随疲劳呈现明显变化。疲劳模型识别结果表明，对于三种机器学习方法，PCA 特征融合均优于 GRA 特征融合，XG-Boost 表现最佳，识别准确率为 89.47%，灵敏度和特异性均为 100%，AUC 为 0.90。SVM 模型也表现出良好的识别性能（准确率 89.47%，AUC=0.89）。RF 模型的性能最差，识别准确率仅为 78.95%。
本研究表明，ECG 和 EMG 的生理指标随疲劳呈现明显、有规律的变化，采用 SVM、RF 和 XG-Boost 模型对矿工疲劳进行识别是可行的。PCA 融合技术比 GRA 方法能提高识别准确率。XG-Boost 分类具有最佳的准确性和鲁棒性。本研究可为高原地区人体疲劳识别的临床研究以及急性高原病和人体高原适应的临床研究提供参考。
