Validity of facial features’ geometric measurements for real-time assessment of mental fatigue in construction equipment operators |
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| Affiliation: | 1. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region;2. Department of Architecture and Built Environment, Northumbria University, NE1 8ST, Newcastle upon Tyne, UK;3. Department of Software Engineering, Fatima Jinnah Women University, Rawalpindi 46000 Pakistan;4. School of Automation, Guangdong University of Petrochemical Technology, Maoming, China;1. College of Management and Economics, Tianjin University, Tianjin 300072, China;2. Department of Civil and Environmental Engineering, University of Alberta, Edmonton T6G 2R3, Canada;3. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region;1. School of Management, Northwestern Polytechnical University, Xi’an, PR China;2. Mechanical Engineering and Design Department, Université de Bourgogne Franche-Comté, Université de technologie de Belfort-Montbéliard, Belfort Cedex, France;3. School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, PR China;4. Guangdong Provincial Key Laboratory of Advanced Welding Technology for Ships, CSSC Huangpu Wenchong Shipbuilding Company Limited, Guangzhou, PR China;1. Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Department of Civil Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China;2. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 1037, Luoyu Road, Hongshan District, Wuhan, Hubei 430074, China;1. Institute of Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, Hubei, China;2. National Center of Technology Innovation for Digital Construction, Huazhong University of Science and Technology, Wuhan, Hubei, China;3. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China;4. Weichai Power Co., Ltd, Weifang, Shandong, China;5. Shantui Construction Machinery Co.,Ltd, Jining, Shandong, China |
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| Abstract: | Operating construction equipment for extended periods of time may lead to mental fatigue and, as a result, an increased risk of human error-related accidents and jeopardized health problems for the operators. Therefore, to limit the risk of accidents and protect operators' wellbeing, their mental fatigue must be monitored reliably and in real time. Recently, many invasive technologies have been employed to alleviate this problem, but they entail the wearing of physical sensors, which may instigate irritation and discomfort. This study proposes a non-invasive mental fatigue monitoring method using geometric measurements of their facial features that does not require the operators to wear sensors on their body. The study further validates the proposed method by comparing it with wearable electroencephalography (EEG) technology to establish its ecological validity for construction equipment operators. To serve the purpose, a one-hour excavator operation by sixteen construction equipment operators was conducted on a construction site. Ground truth, brain activity using wearable EEG, and geometric measurements of facial features were extracted and analyzed at the baseline and every 20 min for one hour. A considerable temporal variation was found in the reported metrics (eye aspect ratio, eye distance, mouth aspect ratio, face area, and head motion) and were significantly correlated with ground truth and EEG metric. Furthermore, the brain visualization pattern obtained from EEG was also associated with the variations in the facial features. The findings of the study reveal that construction equipment operators’ mental fatigue can be monitored non-invasively using geometrical measurements of facial features. |
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| Keywords: | Mental fatigue Construction equipment operators Construction safety Facial features Electroencephalography |
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