Non-invasive detection of mental fatigue in construction equipment operators through geometric measurements of facial features

Mehmood, Imran; Li, Heng; Umer, Waleed; Ma, Jie; Saad Shakeel, Muhammad; Anwer, Shahnawaz; Fordjour Antwi-Afari, Maxwell; Tariq, Salman; Wu, Haitao

doi:10.1016/j.jsr.2024.01.013

Journal of Safety Research

2024

DOI: 10.1016/j.jsr.2024.01.013

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Non-invasive detection of mental fatigue in construction equipment operators through geometric measurements of facial features

Imran Mehmood,

Heng Li,

Waleed Umer

et al.

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2024

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Cited by 5 publications

References 110 publications

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Green buildings: Requirements, features, life cycle, and relevant intelligent technologies

Yin,

Wu,

Zhao

et al. 2024

Internet of Things and Cyber-Physical Systems

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Green buildings: Requirements, features, life cycle, and relevant intelligent technologies

Yin,

Wu,

Zhao

et al. 2024

Internet of Things and Cyber-Physical Systems

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Human Operator Mental Fatigue Assessment Based on Video: ML-Driven Approach and Its Application to HFAVD Dataset

Othman,

Hamoud,

Shilov

et al. 2024

Applied Sciences

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The detection of the human mental fatigue state holds immense significance due to its direct impact on work efficiency, specifically in system operation control. Numerous approaches have been proposed to address the challenge of fatigue detection, aiming to identify signs of fatigue and alert the individual. This paper introduces an approach to human mental fatigue assessment based on the application of machine learning techniques to the video of a working operator. For validation purposes, the approach was applied to a dataset, “Human Fatigue Assessment Based on Video Data” (HFAVD) integrating video data with features computed by using our computer vision deep learning models. The incorporated features encompass head movements represented by Euler angles (roll, pitch, and yaw), vital signs (blood pressure, heart rate, oxygen saturation, and respiratory rate), and eye and mouth states (blinking and yawning). The integration of these features eliminates the need for the manual calculation or detection of these parameters, and it obviates the requirement for sensors and external devices, which are commonly employed in existing datasets. The main objective of our work is to advance research in fatigue detection, particularly in work and academic settings. For this reason, we conducted a series of experiments by utilizing machine learning techniques to analyze the dataset and assess the fatigue state based on the features predicted by our models. The results reveal that the random forest technique consistently achieved the highest accuracy and F1-score across all experiments, predominantly exceeding 90%. These findings suggest that random forest is a highly promising technique for this task and prove the strong connection and association among the predicted features used to annotate the videos and the state of fatigue.

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Three-Dimensional Point Cloud Stitching Method in Infrared Images of High-Voltage Cables

Yu,

Huang,

Cheng

2024

Coatings

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High-voltage power cables are crucial to the normal operation of all electrical equipment. The insulation surrounding these cables is subject to faults. The traditional methods for detecting cable insulation characteristics primarily focus on breakdown performance tests. However, the measurement precision is low, the risk coefficient is high, and the test cost is high. Additionally, it is difficult to precisely pinpoint high-voltage cable faults. Therefore, in this study, a method for inspecting high-voltage cable faults using infrared stereoscopic vision is proposed. This method enables non-contact remote safety measurements to be conducted. For a limited lens angle in an infrared camera, an area matching stitching method that incorporates feature point matching is developed. The key technologies for three-dimensional (3D) point cloud stitching include feature point extraction and image matching. To address the problem of the Harris algorithm not having scale invariance, Gaussian multi-scale transform parameters were added to the algorithm. During the matching process, a random sampling consistency algorithm is used to eliminate incorrect pairs of matching points. Subsequently, a 3D point cloud stitching experiment on infrared cable images was conducted. The feasibility of the stitching algorithm was verified through qualitative and quantitative analyses of the experimental results. Based on the mechanism by which thermal breakdowns occur, a method for detecting anomalous temperatures in cables is developed based on infrared stereoscopic vision. In this manuscript, the infrared technique, 3D point cloud stitching, and cables inspection are combined for the first time. The detection precision is high, which contributes to the development of high-voltage electrical equipment nondestructive testing.

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Non-invasive detection of mental fatigue in construction equipment operators through geometric measurements of facial features

Cited by 5 publications

References 110 publications

Green buildings: Requirements, features, life cycle, and relevant intelligent technologies

Green buildings: Requirements, features, life cycle, and relevant intelligent technologies

Human Operator Mental Fatigue Assessment Based on Video: ML-Driven Approach and Its Application to HFAVD Dataset

Three-Dimensional Point Cloud Stitching Method in Infrared Images of High-Voltage Cables

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