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

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...