Deep learning-based construction equipment operators’ mental fatigue classification using wearable EEG sensor data

Abstract

Operator attention failure due to mental fatigue during extended equipment operations is a common cause of equipment-related accidents that result in catastrophic injuries and fatalities. As a result, tracking operators' mental fatigue is critical to reducing equipment-related accidents on construction sites. Previously, several strategies aimed at recognizing mental fatigue with adequate accuracy, such as machine learning utilizing EEG-based wearable sensing systems, have been proposed. However, the ability to track operators’ mental fatigue for its implementation on an actual construction site is still an issue. For instance, the mobility and systemic instability of EEG sensors necessitate their application in laboratory settings rather than on actual construction sites. Furthermore, while the machine learning classifiers achieved acceptable accuracy, their input is limited to manually developed EEG features, which may compromise the models’ performance on real construction sites. Accordingly, the current research proposes the viability of a construction site strategy that uses flexible headband-based sensors for acquiring raw EEG data and deep learning networks to recognize operators' mental fatigue. To serve this purpose, a one-hour excavator operation by fifteen operators was conducted on a construction site. The NASA-TLX score was used as the ground truth of mental fatigue, and brain activity patterns were recorded using a wearable EEG sensor. The raw EEG data was then used to develop deep learning-based classification models. Finally, the performance of deep learning models, i.e., long short-term memory, bidirectional LSTM, and one-dimensional convolutional networks, was investigated using accuracy, precision, recall, specificity, and an F1-score. The findings indicate that the Bi-LSTM model outperforms the other deep learning models with a high accuracy of 99.941% and F1-score between 99.917% and 99.993%. These findings demonstrate the feasibility of applying the Bi-LSTM model and contribute to wearable sensor-based mental fatigue recognition and classification, thus enhancing on-site health and safety operations.

Publication DOI: https://doi.org/10.1016/j.aei.2023.101978
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Civil Engineering
College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing
Funding Information: The authors acknowledged the following two funding grants: 1. General Research Fund (GRF) Grant (15201621) titled “Monitoring and managing fatigue of construction plant and equipment operators exposed to prolonged sitting”; 2. General Research Fund (GRF)
Additional Information: Copyright © 2023 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. Funding Information: The authors acknowledged the following two funding grants: 1. General Research Fund (GRF) Grant (15201621) titled “Monitoring and managing fatigue of construction plant and equipment operators exposed to prolonged sitting”; 2. General Research Fund (GRF) Grant (15210720) titled “The development and validation of a noninvasive tool to monitor mental and physical stress in construction workers”; and 3. Research Institute for Intelligent Wearable System (RI-IWEAR) - Strategic Supporting Scheme (CD47) titled “An Automated Assessment of Construction Equipment Operators' Mental Fatigue based on Facial Expressions”.
Uncontrolled Keywords: Construction equipment operators,Construction safety,Deep learning networks,Electroencephalography,Mental fatigue,Information Systems,Building and Construction,Artificial Intelligence
Publication ISSN: 1474-0346
Last Modified: 28 Feb 2024 08:31
Date Deposited: 16 May 2023 08:23
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Related URLs: https://www.sci ... 1064?via%3Dihub (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2023-04
Published Online Date: 2023-04-27
Accepted Date: 2023-04-17
Authors: Mehmood, Imran
Li, Heng
Qarout, Yazan
Umer, Waleed
Anwer, Shahnawaz
Wu, Haitao
Hussain, Mudasir
Fordjour Antwi-Afari, Maxwell (ORCID Profile 0000-0002-6812-7839)

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Access Restriction: Restricted to Repository staff only until 27 April 2024.

License: Creative Commons Attribution Non-commercial No Derivatives


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