Search

In the construction industry, the operator's mental fatigue is one of the most important causes of construction equipment-related accidents. Mental fatigue can easily lead to poor performance of construction equipment operations and accidents in the worst case scenario. Hence, it is necessary to propose an objective method that can accurately detect multiple levels of mental fatigue of construction equipment operators. To address such issue, this paper develops a novel method to identify and classify operator's multi-level mental fatigue using wearable eye-tracking technology. For the purpose, six participants were recruited to perform a simulated excavator operation experiment to obtain relevant data. First, a Toeplitz Inverse Covariance-Based Clustering (TICC) method was used to determine the number of levels of mental fatigue using relevant subjective and objective data collected during the experiments. The results revealed the number of mental fatigue levels to be 3 using TICC-based method. Second, four eye movement feature-sets suitable for different construction scenarios were extracted and supervised learning algorithms were used to classify multi-level mental fatigue of the operator. The classification performance analysis of the supervised learning algorithms showed Support Vector Machine (SVM) was the most suitable algorithm to classify mental fatigue in the face of various construction scenarios and subject bias (accuracy between 79.5% and 85.0%). Overall, this study demonstrates the feasibility of applying wearable eye-tracking technology to identify and classify the mental fatigue of construction equipment operators.