Prediction of Mental Fatigue for Control Room Operators: Innovative Data Processing and Multi-Model Evaluation

Yong Chen, Jiangtao Chen, Xian Xie, Wenchao Yi and Zuzhen Ji ()
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Yong Chen: Department of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310000, China
Jiangtao Chen: Department of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310000, China
Xian Xie: Department of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310000, China
Wenchao Yi: Department of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310000, China
Zuzhen Ji: Department of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310000, China

Mathematics, 2025, vol. 13, issue 17, 1-30

Abstract: When control room operators encounter mental fatigue, the accuracy of their work will decline. Accurately predicting the mental fatigue of industrial control room operators is of great significance for preventing operational mistakes. In this study, facial data of experimental participants were collected via cameras, and fatigue levels were evaluated using an improved Karolinska Sleepiness Scale (KSS). Subsequently, a dataset of fatigue samples based on facial features was established. A novel early-warning framework was put forward, framing fatigue prediction as a time series prediction task. Two innovative data processing techniques were introduced. Reverse data binning transforms discrete fatigue labels into continuous values through a random perturbation of ≤0.3, enabling precise temporal modeling. A fatigue-aware data screening method uses the 6 s rule and a sliding window to filter out transient states and preserve key transition patterns. Five prediction models, namely Light Gradient Boosting Machine (LightGBM), Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN), Transformer, and Attention-based Temporal Convolutional Network (Attention-based TCN), were evaluated using the collected dataset of fatigue samples based on facial features. The results indicated that LightGBM demonstrated outstanding performance, with an accuracy rate reaching 93.33% and an average absolute error of 0.067. It significantly outperformed deep learning models. Moreover, its computational efficiency further verified its suitability for real-time deployment. This research integrates predictive modeling with industrial safety applications, providing evidence for the feasibility of machine learning in proactive fatigue management.

Keywords: prediction of mental fatigue; operator; computer vision; machine learning; LightGBM (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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