Identifying fatigue of climbing workers using physiological data based on the XGBoost algorithm

• Published in: Frontiers in public health Vol. 12; p. 1462675
• Main Authors: Xu, Yonggang, Jian, Qingzhi, Zhu, Kunshuang, Wang, Mingjun, Hou, Wei, Gong, Zichao, Xu, Mingkai, Cui, Kai
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 09.10.2024
• Subjects: Adult; Algorithms; Blood Pressure - physiology; climbing workers; Fatigue; fatigue identification; Female; Hand Strength - physiology; Heart Rate - physiology; Humans; machine learning; Male; physiological data; Public Health; Reaction Time - physiology; XGBoost; Young Adult; climbing workers; machine learning; XGBoost; fatigue identification; physiological data
• ISSN: 2296-2565; 2296-2565
• DOI: 10.3389/fpubh.2024.1462675

High-voltage workers often experience fatigue due to the physically demanding nature of climbing in dynamic and complex environments, which negatively impacts their motor and mental abilities. Effective monitoring is necessary to ensure safety. This study proposed an experimental method to quantify fatigue in climbing operations. We collected subjective fatigue (using the RPE scale) and objective fatigue data, including systolic blood pressure (SBP), diastolic blood pressure (DBP), blood oxygen saturation (SpO ), vital capacity (VC), grip strength (GS), response time (RT), critical fusion frequency (CFF), and heart rate (HR) from 33 high-voltage workers before and after climbing tasks. The XGBoost algorithm was applied to establish a fatigue identification model. The analysis showed that the physiological indicators of SpO , VC, GS, RT, and CFF can effectively evaluate fatigue in climbing operations. The XGBoost fatigue identification model, based on subjective fatigue and the five physiological indicators, achieved an average accuracy of 89.75%. This study provides a basis for personalized management of fatigue in climbing operations, enabling timely detection of their fatigue states and implementation of corresponding measures to minimize the likelihood of accidents.