A Bibliometric Analysis of Neuroscience Tools Use in Construction Health and Safety Management

被引:1
|
作者
Ding, Zhikun [1 ,2 ,3 ,4 ]
Xiong, Zhaoyang [2 ,3 ]
Ouyang, Yewei [5 ]
机构
[1] Shenzhen Univ, Key Lab Coastal Urban Resilient Infrastruct, Minist Educ, Shenzhen 518060, Peoples R China
[2] Guangdong Lab Artificial Intelligence & Digital Ec, Shenzhen 518060, Peoples R China
[3] Shenzhen Univ, Sino Australia Joint Res Ctr BIM & Smart Construct, Shenzhen 518060, Peoples R China
[4] Shenzhen Univ, Shenzhen Key Lab Green Efficient & Intelligent Con, Shenzhen 518060, Peoples R China
[5] City Univ Hong Kong, Dept Architecture & Civil Engn, Kowloon, Hong Kong, Peoples R China
关键词
construction health and safety management (CHSM); neuroscience; bibliometric analysis; hazard recognition; fatigue monitoring; MUSCULOSKELETAL DISORDERS; HAZARD RECOGNITION; WORKERS; RISK; FATIGUE; STRESS; SITE; VIGILANCE;
D O I
10.3390/s23239522
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Despite longstanding traditional construction health and safety management (CHSM) methods, the construction industry continues to face persistent challenges in this field. Neuroscience tools offer potential advantages in addressing these safety and health issues by providing objective data to indicate subjects' cognition and behavior. The application of neuroscience tools in the CHSM has received much attention in the construction research community, but comprehensive statistics on the application of neuroscience tools to CHSM is lacking to provide insights for the later scholars. Therefore, this study applied bibliometric analysis to examine the current state of neuroscience tools use in CHSM. The development phases; the most productive journals, regions, and institutions; influential scholars and articles; author collaboration; reference co-citation; and application domains of the tools were identified. It revealed four application domains: monitoring the safety status of construction workers, enhancing the construction hazard recognition ability, reducing work-related musculoskeletal disorders of construction workers, and integrating neuroscience tools with artificial intelligence techniques in enhancing occupational safety and health, where magnetoencephalography (EMG), electroencephalography (EEG), eye-tracking, and electrodermal activity (EDA) are four predominant neuroscience tools. It also shows a growing interest in integrating the neuroscience tools with artificial intelligence techniques to address the safety and health issues. In addition, future studies are suggested to facilitate the applications of these tools in construction workplaces by narrowing the gaps between experimental settings and real situations, enhancing the quality of data collected by neuroscience tools and performance of data processing algorithms, and overcoming user resistance in tools adoption.
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页数:30
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