Journal of occupational and environmental medicine · 2026
Distinct mental fatigue mechanisms under prolonged work and night shifts Implications for safety management in a real-world oil drilling site.
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Hao Su, Xin Qing, Xiandeng He, Jian Wang, Hongbin Cai, Xiaoqin Wang, Lu Liu, Yuqi Song, Yuhang Zeng, Yuxi Zeng
Аннотация
OBJECTIVE: This study aimed to investigate how shift work and prolonged working hours affect mental fatigue under real-world working conditions, focusing on oil and gas drilling operations.
METHODS: Physiological data from drillers, collected via wearable sensors, were analyzed. EEG features and heart rate data were integrated, and a semi-supervised machine learning model was developed to classify mental fatigue.
RESULTS: The study revealed distinct heart rate changes under prolonged work and night-shift conditions, reflecting parasympathetic regulation and circadian disruption. EEG analysis showed significant alterations in the α band and increased functional connectivity in the left frontal lobe. The machine learning model achieved a 93.46% classification accuracy.
CONCLUSIONS: Night-shift work induces different fatigue characteristics from day shifts, necessitating tailored safety management strategies. The fatigue recognition method offers a proactive approach for early intervention in complex operational environments.
METHODS: Physiological data from drillers, collected via wearable sensors, were analyzed. EEG features and heart rate data were integrated, and a semi-supervised machine learning model was developed to classify mental fatigue.
RESULTS: The study revealed distinct heart rate changes under prolonged work and night-shift conditions, reflecting parasympathetic regulation and circadian disruption. EEG analysis showed significant alterations in the α band and increased functional connectivity in the left frontal lobe. The machine learning model achieved a 93.46% classification accuracy.
CONCLUSIONS: Night-shift work induces different fatigue characteristics from day shifts, necessitating tailored safety management strategies. The fatigue recognition method offers a proactive approach for early intervention in complex operational environments.