Validity of facial features’ geometric measurements for real-time assessment of mental fatigue in construction equipment operators

Mehmood, Imran, Li, Heng, Umer, Waleed, Arsalan, Aamir, Saad Shakeel, M. and Anwer, Shahnawaz (2022) Validity of facial features’ geometric measurements for real-time assessment of mental fatigue in construction equipment operators. Advanced Engineering Informatics, 54. p. 101777. ISSN 1474-0346

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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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