TY - JOUR
T1 - Assessment of Construction Workers’ Spontaneous Mental Fatigue Based on Non-Invasive and Multimodal In-Ear EEG Sensors
AU - Fang, Xin
AU - Li, Heng
AU - Ma, Jie
AU - Xing, Xuejiao
AU - Fu, Zhibo
AU - Antwi-Afari, Maxwell Fordjour
AU - Umer, Waleed
PY - 2024/9/5
Y1 - 2024/9/5
N2 - Construction activities are often conducted in outdoor and harsh environments and involve long working hours and physical and mental labor, which can lead to significant mental fatigue among workers. This study introduces a novel and non-invasive method for monitoring and assessing mental fatigue in construction workers. Based on cognitive neuroscience theory, we analyzed the neurophysiological mapping of spontaneous mental fatigue and developed multimodal in-ear sensors specifically designed for construction workers. These sensors enable real-time and continuous integration of neurophysiological signals. A cognitive experiment was conducted to validate the proposed mental fatigue assessment method. Results demonstrated that all selected supervised classification models can accurately identify mental fatigue by using the recorded neurophysiological data, with evaluation metrics exceeding 80%. The long short-term memory model achieved an average accuracy of 92.437%. This study offers a theoretical framework and a practical approach for assessing the mental fatigue of on-site workers and provides a basis for the proactive management of occupational health and safety on construction sites.
AB - Construction activities are often conducted in outdoor and harsh environments and involve long working hours and physical and mental labor, which can lead to significant mental fatigue among workers. This study introduces a novel and non-invasive method for monitoring and assessing mental fatigue in construction workers. Based on cognitive neuroscience theory, we analyzed the neurophysiological mapping of spontaneous mental fatigue and developed multimodal in-ear sensors specifically designed for construction workers. These sensors enable real-time and continuous integration of neurophysiological signals. A cognitive experiment was conducted to validate the proposed mental fatigue assessment method. Results demonstrated that all selected supervised classification models can accurately identify mental fatigue by using the recorded neurophysiological data, with evaluation metrics exceeding 80%. The long short-term memory model achieved an average accuracy of 92.437%. This study offers a theoretical framework and a practical approach for assessing the mental fatigue of on-site workers and provides a basis for the proactive management of occupational health and safety on construction sites.
KW - deep learning
KW - cognitive neuroscience
KW - in-ear sensors
KW - construction safety
KW - mental fatigue monitoring
UR - http://www.scopus.com/inward/record.url?scp=85205218401&partnerID=8YFLogxK
U2 - 10.3390/buildings14092793
DO - 10.3390/buildings14092793
M3 - Article
SN - 2075-5309
VL - 14
JO - Buildings
JF - Buildings
IS - 9
M1 - 2793
ER -