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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.

A novel prism-chamber assembly was prepared for application in optical waveguide based chemical and biological sensors, making the sensor easily and reproducibly operate. By using the prism-chamber assembly, the performance of a composite waveguide based integrated Young interferometer sensor was investigated. The temporal interference pattern detected with a single-slit photodetector heavily relies on the slit width, and regular high-contrast patterns can be obtained under the condition that the slit width is smaller than the spatial periodicity of the sensor. Increasing the temperature of water in the chamber leads to a quasi-linear variation in the phase difference with Deltaphi/DeltaT approximately -9.1 degrees/degrees C. Significant dependence of the sensor's sensitivity on the polarization state of the guided mode was also observed. The sensor is stable and reliable, capable of real-time detection of very slow bioreactions at the interface.