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

Construction equipment operators Construction health and safety Electrodermal activity Face landmarks Facial features Mental fatigue

Journal

Journal of safety research
ISSN: 1879-1247
Titre abrégé: J Safety Res
Pays: United States
ID NLM: 1264241

Informations de publication

Date de publication:
Jun 2024
Historique:
received: 15 07 2023
revised: 17 10 2023
accepted: 26 01 2024
medline: 11 6 2024
pubmed: 11 6 2024
entrez: 10 6 2024
Statut: ppublish

Résumé

Prolonged operation of construction equipment could lead to mental fatigue, which can increase the chances of human error-related accidents as well as operators' ill-health. The objective detection of operators' mental fatigue is crucial for reducing accident risk and ensuring operator health. Electroencephalography, photoplethysmography, electrodermal activity, and eye-tracking technology have been used to mitigate this issue. These technologies are invasive and wearable sensors that can cause irritation and discomfort. Geometric measurements of facial features can serve as a noninvasive alternative approach. Its application in detecting mental fatigue of construction equipment operators has not been reported in the literature. Although the application of facial features has been widespread in other domains, such as drivers and other occupation scenarios, their ecological validity for construction excavator operators remains a knowledge gap. This study proposed employing geometric measurements of facial features to detect mental fatigue in construction equipment operators' facial features. In this study, seventeen operators performed excavation operations. Mental fatigue was labeled subjectively and objectively using NASA-TLX scores and EDA values. Based on geometric measurements, facial features (eyebrow, mouth outer, mouth corners, head motion, eye area, and face area) were extracted. The results showed that there was significant difference in the measured metrics for high fatigue compared to low fatigue. Specifically, the most noteworthy variation was for the eye and face area metrics, with mean differences of 45.88% and 26.9%, respectively. The findings showed that geometrical measurements of facial features are a useful, noninvasive approach for detecting the mental fatigue of construction equipment operators.

Identifiants

pubmed: 38858047
pii: S0022-4375(24)00013-6
doi: 10.1016/j.jsr.2024.01.013
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

234-250

Informations de copyright

Copyright © 2024 National Safety Council and Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Imran Mehmood (I)

Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region. Electronic address: imran.mehmood@connect.polyu.hk.

Heng Li (H)

Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region. Electronic address: heng.li@polyu.edu.hk.

Waleed Umer (W)

Department of Architecture and Built Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, United Kingdom. Electronic address: waleed.umer@northumbria.ac.uk.

Jie Ma (J)

Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region. Electronic address: jie2021.ma@connect.polyu.hk.

Muhammad Saad Shakeel (M)

School of Automation Science and Engineering, South China University of Technology, Guangzhou, China. Electronic address: saad.shakeel@gdupt.edu.cn.

Shahnawaz Anwer (S)

Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region. Electronic address: shah-nawaz.anwer@polyu.edu.hk.

Maxwell Fordjour Antwi-Afari (M)

Department of Civil Engineering, College of Engineering and Physical Sciences, Aston University, Birmingham, B4 7ET, United Kingdom. Electronic address: m.antwiafari@aston.ac.uk.

Salman Tariq (S)

Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region. Electronic address: salman.tariq@polyu.edu.hk.

Haitao Wu (H)

Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region. Electronic address: haitao.wu@connect.polyu.hk.

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