A Bibliometric Analysis of Neuroscience Tools Use in Construction Health and Safety Management
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Framework
2.2. Publications Search and Selection
2.2.1. Databases Accessed
2.2.2. Search Strategy
2.2.3. Include & Exclude Criteria
2.2.4. Science Mapping
3. Results and Analysis
3.1. Development Phases during 2001 to 2023
3.2. Publication Sources over the Years: Journals, Regions, and Institutions
3.2.1. Journal Analysis
3.2.2. Country (Region) and Institution Analysis
3.3. Influential Scholars and Author Collaboration
3.3.1. Influential Scholars
3.3.2. Author Collaboration
3.4. Highly Cited Articles and Reference Co-Citation Analysis
3.4.1. Top 10 Most-Cited Papers
3.4.2. Reference Co-Citation Analysis
3.5. Application Domains of Neuroscience Tools in CHSM
3.5.1. Monitoring Workers’ Safety Status
3.5.2. Enhancing Workers’ Hazard Identification Ability
3.5.3. Reducing Work-Related Muscle Skeleton Disorders of Construction Workers
3.5.4. Integrating with Artificial Intelligence (AI) to Address the Safety and Health Issues
3.6. Application Topics of the Primary Neuroscience Tools in CHSM
3.6.1. EMG
3.6.2. EEG
3.6.3. Eye-Tracking
3.6.4. EDA
4. Discussion
4.1. Narrowing the Gaps between Experimental Settings and Real Situations
4.2. Enhancing the Quality of Data Collected by Neuroscience Tools and Performance of Data Processing Algorithms
4.3. Overcoming User Resistance in Tool Adoption
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Database | Search Query |
---|---|
Web of Science (n = 53) Scopus (n = 185) PubMed (n = 21) | (“construction industry” OR “construction sector” OR “construction organization” OR “building industry” OR “building project”) AND (“construction safe *” OR “worker safe *” OR “labor safe *” OR “accident perception” OR “collision perception” OR “risk recognition” OR “hazard recognition” OR “danger recognition” OR “vigilance” OR “attention” OR “alertness” OR “notice” OR “cognitive status” OR “cognitive state” OR “mental status” OR “mental state” OR “brain status” OR “brain state” OR “psychological status” OR “psychological state” OR “fatigue” OR “exhaustion” OR “work load” OR “mental load” OR “cognitive load” OR “stress” OR “tension” OR “emotion” OR “occupational health *” OR “physical strain” OR “ergonomic risk” OR “muscle fatigue” OR “musculoskeletal disorder” OR “spinal compression” OR “injury” OR “illness” OR “therapy” OR “interference” OR “recovery”) AND (“Electroencephalogram” OR “EEG” OR “eye tracking” OR “ET” OR “event related potential” OR “ERP” OR “Electrocardiogram” OR “ECG” OR “Electromyography” OR “electromyogram” OR “EMG” OR “Electrodermal activity” OR “EDA” OR “Photoplethysmography” OR “PPG” OR “functional near-infrared spectroscopy” OR “fNIRS” OR “functional magnetic resonance imaging” OR “fMRI” OR “magnetoencephalography” OR “MEG”) |
Institute | Number of Publications | Total Citations |
---|---|---|
City University of Hong Kong | 4 | 295 |
Tsinghua University | 4 | 55 |
EMGO+ Institute for Health and Care Research | 3 | 128 |
Purdue University | 3 | 43 |
University of Florida | 3 | 31 |
Coronel Institute of Occupational Health | 2 | 105 |
Huazhong University of Science and Technology | 2 | 116 |
National Institute of Occupational Health | 2 | 54 |
North Carolina State University | 2 | 88 |
The Hong Kong Polytechnic University | 2 | 91 |
University of Michigan | 2 | 107 |
University of Nebraska-Lincoln | 2 | 166 |
University of Waterloo | 2 | 32 |
Author | TC | NP | H-Index | Ave. Pub. Year | Affiliation | Research Subject |
---|---|---|---|---|---|---|
Li, Heng | 273 | 5 | 4 | 2020 | The Hong Kong Polytechnic University, Hong Kong SAR, China | Construction Informatics, Construction Engineering and Management, Construction Health and Safety |
Berardi, Umberto | 272 | 1 | 1 | 2012 | Toronto Metropolitan University, Canada | Sustainability, Energy Efficiency, Acoustics, Urban Resiliency, Building Science |
Chen, Jiayu | 263 | 4 | 5 | 2017 | City University of Hong Kong, Hong Kong SAR, China | Human-centric Mental Sensing, Human-Robot Collaboration, Building Digital Twin, Smart Urban Grid and Simulation |
Esmaeili, Behzad | 258 | 4 | 5 | 2018 | Purdue University, United States | Construction Safety, Risk Management, Decision Making |
Hasanzadeh, Sogand | 257 | 4 | 4 | 2019 | Purdue University, United States | Smart Safety, Wearable TechAR/VR/MR, Human-AI Teaming, Human Factor |
Wang, Di | 143 | 2 | 2 | 2018 | University of Michigan, United States | Deep Learning, Signal Processing, Mutual Information |
Choi, Byungjoo | 137 | 3 | 3 | 2020 | Ajou University, South Korea | Construction Engineering and Management, Construction Automation, ICT in Construction, Smart Construction, Construction Safety |
Jebelli, Houtan | 121 | 3 | 4 | 2020 | Pennsylvania State University, United States | Construction Robotics, Construction Automation, Human Robot Collaboration, Wearable Technologies, Engineering Education |
Van Der Molen, Henk F | 118 | 3 | 3 | 2013 | VU University Medical Center, Netherlands | Construction Worker, Musculoskeletal Disorders, Work-related Risk Factors, Preventive Measures, Physical and Mental Health |
Dzeng, Ren-Jye | 112 | 2 | 1 | 2016 | National Chiao Tung University, Taiwan, China | Construction Management, Artificial Intelligence, Mobile App, Sensor, Eye tracker |
Lin, Chin-Teng | 111 | 1 | 1 | 2016 | National Chiao Tung University, Taiwan, China | Computational Intelligence, Machine Learning, Fuzzy Neural Networks, Cognitive Neuro-Engineering, Brain-Computer Interface |
Song, Xinyi | 111 | 1 | 2 | 2016 | Georgia Institute of Technology, United States | Building Energy Efficiency, Occupational Health, Facility Management, Construction Safety, Construction Dispute |
Boschman, Julitta S. | 110 | 2 | 2 | 2012 | University of Amsterdam, Netherlands | Musculoskeletal Disorders, Occupational Health, Work-related Health Surveillance, Processing Assessment |
Albert, Alex | 92 | 3 | 3 | 2019 | North Carolina State University, United States | Construction Safety, Injury Prevention, Risk Management, Hazard Recognition, Safety Interventions |
Yu, Yantao | 91 | 1 | 1 | 2019 | The Hong Kong Polytechnic University, Hong Kong SAR, China | Construction Informatics, Wearable Device, Behavior Recognition |
Author | Title | Source | Year | TC | Research Focus |
---|---|---|---|---|---|
Dzeng R. J.; Lin C. T.; Fang Y. C. [20] | Using eye-tracker to compare search patterns between experienced and novice workers for site hazard identification | Safety Science | 2016 | 111 | Examining the impact of experience on hazard identification in digital construction sites using eye-tracking, highlighting the difference between speed and accuracy. |
Chen J.; Song X.; Lin Z. [45] | Revealing the “invisible Gorilla” in construction: Estimating construction safety through mental workload assessment | Automation in Construction | 2016 | 100 | Examining the role of inattentional blindness on hazard identification in construction sites using eye-tracking and EEG analysis. |
Wang D.; Chen J.; Zhao D.; Dai F.; Zheng C.; Wu X. [14] | Monitoring workers’ attention and vigilance in construction activities through a wireless and wearable electroencephalography system | Automation in Construction | 2017 | 98 | Exploring the effectiveness of a wearable EEG system in evaluating the attention of construction workers. |
Boschman J.S.; Van Der Molen H.F.; Sluiter J.K.; Frings-Dresen M.H. [54] | Musculoskeletal disorders among construction workers: A one-year follow-up study | BMC Musculoskeletal Disorders | 2012 | 97 | Using questionnaires to assess the prevalence, work-relatedness, and work problems of musculoskeletal disorders among different construction occupations to examine the value of choosing preventive measures. |
Hasanzadeh S.; Esmaeili B.; Dodd M.D. [21] | Measuring the impacts of safety knowledge on construction workers’ attentional allocation and hazard detection using remote eye-tracking technology | Journal of Management in Engineering | 2017 | 95 | Employing eye-tracking to examine the effects of safety knowledge on hazard detection among construction workers. |
Yu Y.; Li H.; Yang X.; Kong L.; Luo X.; Wong A.Y.L. [55] | An automatic and non-invasive physical fatigue assessment method for construction workers | Automation in Construction | 2019 | 91 | Introducing a computer vision technique to assess construction workers’ physical fatigue through 3D motion capture and biomechanical analysis. |
Hasanzadeh S.; Esmaeili B.; Dodd M.D. [22] | Examining the relationship between construction workers’ visual attention and situation awareness under fall and tripping hazard conditions: Using mobile eye tracking | Journal of Construction Engineering and Management | 2018 | 87 | Studying the relationship between construction workers’ attention and situational awareness using eye-tracking. |
Jeelani I.; Albert A.; Han K.; Azevedo R. [57] | Are visual search patterns predictive of hazard recognition performance? Empirical investigation using eye-tracking technology | Journal of Construction Engineering and Management | 2018 | 70 | Using eye-tracking to analyze the link between construction workers’ visual patterns and hazard recognition performances and the impact of tailored training on enhancing these patterns and performance. |
Jebelli H.; Choi B.; Lee S.H. [58] | Application of wearable biosensors to construction sites. I: Assessing workers’ stress | Journal of Construction Engineering and Management | 2019 | 66 | Creating a framework using wearable sensors and machine learning to continuously predict construction workers’ stress from physiological signals. |
Umer W.; Li H.; Szeto G.P.Y.; Wong A.Y.L. [59] | Identification of biomechanical risk factors for the development of lower-back disorders during manual rebar tying | Journal of Construction Engineering and Management | 2017 | 63 | Analyzing lumbar biomechanics in various postures during rebar tying, highlighting stooping’s role in increased lower back disorders in rebar workers. |
Cluster | Keywords |
---|---|
1 | occupational safety (0.28) EEG (0.22) wearable biosensor (0.09) physiological signals (0.06) activity recognition (0.03) aerobic fatigue threshold (0.03) aging (0.03) attention (0.03) construction labor shortage (0.03) fatigue monitoring (0.03) muscle activity (0.03) oxygen prediction (0.03) scaffold building (0.03) inattentional blindness (0.03) occupational stress (0.03) supervised learning (0.03) brain signal processing (0.02) construction worker physical demand (0.02) construction workers’ stress prediction (0.02) performance (0.02) alarm sound (0.02) alert fatigue (0.02) cognitive ability (0.02) construction activity classification (0.02) data quality (0.02) |
2 | eye-tracking (0.18) hazard recognition (0.16) risk management (0.14) mental health (0.10) virtual reality (0.06) safety training (0.04) working memory (0.04) occupational accident (0.04) aviation (0.04) cognitive load (0.04) labor and personnel issues (0.04) maritime (0.04) training (0.04) visual attention (0.04) decision dynamics (0.03) mixed reality (0.03) age (0.03) disabilities (0.03) experience (0.03) individual characteristics (0.03) inspection (0.03) sleep disorders (0.03) |
3 | occupational health (0.13) musculoskeletal disorders (0.13) construction worker (0.11) EMG (0.10) low back pain (0.07) ergonomic (0.06) shoulder pain (0.06) back pain (0.04) endurance (0.04) fatigue (0.04) modelling (0.04) recovery (0.04) participatory ergonomics (0.04) action research (0.04) economics (0.04) fatigue reactions (0.04) heavy industries (0.04) musculoskeletal pain (0.04) organizational ergonomics (0.04) postural stability (0.04) construction noise (0.03) environmental pollution (0.03) dietary behaviour (0.03) energy balance related behaviour (0.03) |
4 | construction industry (0.31) machine learning (0.08) time pressure (0.05) prevention (0.04) deep learning (0.04) computer vision (0.04) mental fatigue (0.03) panel data (0.03) construction equipment operator (0.03) noise-induced hearing loss (0.02) occupational hearing conservation (0.02) screening (0.02) self-administered (0.02) speech reception threshold (0.02) speech-in-noise (0.02) accident (0.02) disability (0.02) handicap (0.02) involvement (0.02) lean (0.02) matrix (0.02) workforce (0.02) correlation and influence (0.02) disaggregation (0.02) emotional exhaustion (0.02) |
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Ding, Z.; Xiong, Z.; Ouyang, Y. A Bibliometric Analysis of Neuroscience Tools Use in Construction Health and Safety Management. Sensors 2023, 23, 9522. https://doi.org/10.3390/s23239522
Ding Z, Xiong Z, Ouyang Y. A Bibliometric Analysis of Neuroscience Tools Use in Construction Health and Safety Management. Sensors. 2023; 23(23):9522. https://doi.org/10.3390/s23239522
Chicago/Turabian StyleDing, Zhikun, Zhaoyang Xiong, and Yewei Ouyang. 2023. "A Bibliometric Analysis of Neuroscience Tools Use in Construction Health and Safety Management" Sensors 23, no. 23: 9522. https://doi.org/10.3390/s23239522