Psychophysiological data-driven multi-feature information fusion and recognition of miner fatigue in high-altitude and cold areas

• Published in: Computers in biology and medicine Vol. 133; p. 104413
• Main Authors: Chen, Shoukun, Xu, Kaili, Yao, Xiwen, Zhu, Siyi, Zhang, Bohan, Zhou, Haodong, Guo, Xin, Zhao, Bingfeng
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.06.2021; Elsevier Limited
• Subjects: Altitude; Blood pressure; Classification; Clinical medicine; Cold; Correlation analysis; Data integration; EKG; Electrocardiography; Electrocardiography (ECG); Electrodes; Electroencephalography; Electromyography; Electromyography (EMG); Exercise; Experiments; Fatigue; Fatigue recognition; Fatigue tests; Feature extraction; Feature recognition; Field study; Field tests; Heart rate; High altitude; High-altitude environments; Human error; Hypoxia; Information fusion; Internal Medicine; Learning algorithms; Machine learning; Mining accidents & safety; Nervous system; Other; Performance evaluation; Physiology; Psychophysiological; Reaction time; Support vector machines; China; Fatigue recognition; Psychophysiological; Electrocardiography (ECG); Electromyography (EMG); Information fusion; Machine learning; psychophysiological
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2021.104413

Fatigue-induced human error is a leading cause of accidents. The purpose of this exploratory study in China was to perform field tests to measure fatigue psychophysiological parameters, such as electrocardiography (ECG), electromyography (EMG), pulse, blood pressure, reaction time and vital capacity (VC), in miners in high-altitude and cold areas and to perform multi-feature information fusion and fatigue identification. Forty-five miners were randomly selected as subjects for a field test, and feature signals were extracted from 90 psychophysiological features as basic signals for fatigue analysis. Fatigue sensitivity indices were obtained by Pearson correlation analysis, t-test and receiver operating characteristic (ROC) curve performance evaluation. The ECG time-domain, ECG frequency-domain, EMG, VC, systolic blood pressure (SBP), and pulse were significantly different after miner fatigue. The support vector machine (SVM) and random forest (RF) techniques were used to classify and identify fatigue by information fusion and factor combination. The optimal fatigue classification factors were ECG-FD (CV Accuracy = 85.0%) and EMG (CV Accuracy = 90.0%). The optimal combination of factors was ECG-TD + ECG-FD + EMG (CV accuracy = 80.0%). Furthermore, SVM machine learning had a good recognition effect. This study shows that SVM and RF can effectively identify miner fatigue based on fatigue-related factor combinations. ECG-FD and EMG are the best indicators of fatigue, and the best performance and robustness are obtained with three-factor combination classification. This study on miner fatigue identification provides a reference for research on clinical medicine and the identification of human fatigue under high-altitude, cold and low-oxygen conditions. •This is the first field test and study on psychophysiological fatigue indicators for miners in high-altitude.•ECG, EMG, blood pressure, reaction time, pulse, and vital capacity multi-feature signal acquisition and preprocessing.•We combined multi-indicator information fusion to analyze fatigue characteristics and perform fatigue recognition.•Fatigue sensitivity indices and a multifactor fatigue identification method were proposed for miners in high-altitude.