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Review

Review of the Application of Wearable Devices in Construction Safety: A Bibliometric Analysis from 2005 to 2021

by
Ran Gao
1,
Bowen Mu
1,
Sainan Lyu
2,
Hao Wang
1,* and
Chengdong Yi
1
1
School of Management Science and Engineering, Central University of Finance and Economics, Beijing 102206, China
2
School of Civil Engineering, Hefei University of Technology, Hefei 230002, China
*
Author to whom correspondence should be addressed.
Buildings 2022, 12(3), 344; https://doi.org/10.3390/buildings12030344
Submission received: 27 January 2022 / Revised: 21 February 2022 / Accepted: 9 March 2022 / Published: 11 March 2022
(This article belongs to the Special Issue Towards Smart Tech 4.0 in the Built Environment: Applications of Disruptive Digital Technologies in Smart Cities, Construction, and Real Estate)
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Abstract

:
Wearable devices as an emerging technology to collect safety data on construction site is gaining increasing attention from researchers and practitioners. Given the rapid development of wearable devices research and the high application prospects of wearable devices in construction safety, a state-of-the-art review of research and implementations in this field is needed. The aim of this study is to provide an objective and extensive bibliometric analysis of the published articles on wearable applications in construction safety for the period of 2005–2021. CiteSpace software was used to conduct co-citation analysis, co-occurrence analysis, and cluster identification on 169 identified articles. The results show that 10 research clusters (e.g., attentional failure, brain-computer interface) were extremely important in the development of wearable devices for construction safety. The results highlight the evolution of wearable devices in construction-safety-related research, revealing the underlying structure of this cross-cutting research area. The analysis also summarizes the status quo of wearable devices in the construction safety field and provides a dynamic platform for integrating future applications.

1. Introduction

The construction industry has long been regarded as one of the most dangerous industries worldwide [1,2]. It employs approximately 7% of the global workforce but contributes to 30–40% of total fatalities [3]. Among all causes of construction accidents, unsafe behaviors of construction workers are the primary and immediate causes. For instance, a study has reported that 88% of accidents are related to unsafe behaviors [4]. To reduce unsafe behaviors and improve the safety performance of construction workers, various measures have been proposed, such as establishing multiple training programs, applying academic knowledge to work sites, and exploring new technologies [5,6]. Wearable devices that offer a promising solution for construction safety management and risk identification are increasingly adopted on construction sites [7]. Due to the dynamic and transient nature of construction [8], traditional manual collection of construction safety data is time intensive [9], and needs to be automated by an effective tool that provides timely information for safety managers to take positive actions. As an emerging technology, wearable devices can potentially realize real-time and accurate security monitoring [10]. They are products controlled by electronic components and software that can be incorporated into clothing or worn on the body like accessories. Wearable devices collect information through tiny, easily worn sensors [11]. Such non-invasive devices avoid the obvious problems of large and complex physical examination devices [12,13], and provide real-time information interaction with the wearer [7]. Timely monitoring and feedback ensure the effectiveness of the information provision. Automated safety monitoring systems based on wearable devices are another promising avenue of research. The data of construction sites collected through wearable devices have been evaluated by researchers and practitioners, providing early warnings of the safety risks in construction environments [14,15,16], physiological signals from construction workers [6,17,18] and automatic recognition of workers’ actions [11,19]. Wearable devices have also assisted safety training [20] and accident prevention [21]. For example, the biomechanical gait-stability parameters can prevent falling and colliding accidents, which are common occurrences on construction sites [21,22].
To reveal the possible connections among the studies on wearable-device applications in construction safety, some studies have reviewed the past development and proposed new research trends in this area. For example, Wang et al. (2015) reviewed the available techniques for the risk assessment of work-related musculoskeletal disorders, and summarized the advantages and limitations of wearable-device systems in this theme [23]. Awolusi et al. (2018) reviewed the application of wearable technologies in construction-safety monitoring, and analyzed the relevant safety performance metrics [24]. Ahn et al. (2019) recently reviewed and identified general wearable-sensing technology applications in construction safety and health, and indicated the challenges and future research opportunities for advancing this field [6]. However, these reviews are often qualitative or based on manual process and researchers’ subjective judgement. Such methods may overlook some articles available for review and thus be vulnerable to biases.
This study aims to conduct a comprehensive and objective bibliometric analysis of the research on the application of wearable devices in construction safety from 2005 to 2021 with the help of CiteSpace software. This research clusters the applications of wearable devices in construction safety, reviews the whole development framework, and suggests future research trends. Based on the bibliometrics, the study quantitatively summarizes the status quo and establishes the important issues concerning the new technologies of wearable devices in construction safety. The research hotspots are illustrated on visual maps. This study extends traditional literature review methods to carry out a bibliometric analysis to delineate the intellectual structure and quantitatively summarizes the related knowledge in graphical form.

2. Research Method

2.1. Data Collection

The data collection consisted of two stages. In Stage 1, a comprehensive search was carried out in the online academic database. This study used Scopus database for literature search, as it could provide a comprehensive coverage of the sciences, social sciences, arts, and humanities across journals, books, and conference proceedings and was sufficiently large for most bibliometric analysis. Articles containing the specific terms in the title/abstract/keyword’ were firstly retrieved. Five experts were interviewed to provide the key search words for the research topic. Based on the analysis of interviews, the following search string was used in the ‘title/abstract/keyword’ fields: (“Wearable devices” OR “Wearable systems” OR “Wearable technology” OR “Wearable sensor”) AND (“construction safety”). The search was further refined by limiting the time span into the recent 16 years—‘from 2005 to 2021’ and the document type to ‘article, review and conference paper’. At the same time, the same search string was used in the Web of Science database to find the articles not included in the Scopus database. A total of 239 articles were identified in this stage.
Given that the search results of Stage 1 may include irrelevant papers that contain the search keywords but do not actually focus on wearable devices in construction safety, Stage 2 was conducted to eliminate the irrelevant literature. To ensure the accuracy and relevance of data, the titles and abstracts of all 239 articles retrieved in Stage 1 were carefully scrutinized by authors. During this process, two authors examined these articles independently and their results of screening were compared and consolidated. After removing 70 irrelevant articles, 169 papers were retained as the basis of our bibliometric analysis. The bibliographic records of the identified 169 literatures were downloaded from the database, including the article title, article type, a list of authors, a set of keywords, the abstract, the journal name, publication year, volume, issue number, number of citations, and a list of the cited references. These bibliographic records were further standardized (e.g., correcting the different spellings of authors, journal, or keywords) through manually checking for bibliometric analysis.

2.2. Bibliometric Analysis

The term of bibliometric was first introduced as “the application of mathematical and statistical methods to books and other means of communication” by Pritchard (p. 349) [25]. Bibliometric analysis is a quantitative statistical analysis of the literature [26], which has been widely used for identifying the relationships among authors, institutions, research directions, and other variables [27,28,29]. A bibliometric analysis can be realized through visualization tools to identify the emerging trends and knowledge structures in a specific research field. The results are often presented intuitively in visual maps. The present study focuses on author co-citation analysis, keyword co-occurrence network, and cluster identification. These techniques are advantageous over the conventional manual review method.
Co-citation analysis measures the semantic similarity among documents, authors, or journals by computing their co-citation relationships. A co-citation relationship defines the frequency at which two items are cited together [30]. Co-citation analysis assumes that when two items are commonly cited together, their contents are relevant to each other. Co-citation analysis can be performed on documents, authors, or journals, which connects the cited documents, authors, or journals that researchers consider as valuable and interesting. The present study conducts an author co-citation analysis, which identifies the relationships among authors whose publications are cited in the same literature. More specifically, an author co-citation analysis identifies and visualizes the knowledgeable structure of a specialist research area by counting the co-citation frequency of two authors’ publications among the reference lists of cited literature [31].
Keywords represent the core content of a research article. A keyword co-occurrence network constructs and maps the knowledge domain of a particular area over a specific time span. This method acknowledges that when keywords frequently co-occur in publications, their underlying ideas are closely associated [32]. A keyword co-occurrence network constructs a similarity measure from the literature contents themselves, rather than linking the literature indirectly through citations. In the present analysis, the bibliometric analysis results of wearable devices in construction safety were demonstrated in a keyword network, which identifies the keywords that co-occur in at least two different articles in a given time span. High-frequency keywords are recognized as indicators of research hotspots or directions over a specified period.
Cluster analysis is commonly applied in knowledge discovery, which identifies the profound themes hidden in the textual data [33]. Cluster analysis categorizes a mass of data into different units with common relevancy of terms, which identifies the research topics and their interrelation within a research domain. In cluster analysis, the homogeneity or consistency of clusters is evaluated from the mean silhouette of the network [34]. When the silhouette value is 1, the clusters in the network are completely separated. Research trends can also be effectively analyzed by cluster analysis [35].
At present, there are many widely used bibliometric tools, such as CiteSpace, VOSViewer, and HistCite. In terms of cluster analysis, VOSViewer does not have as many algorithms as CiteSpace to extract cluster labels. HistCite is relatively simple to operate, but its graphical presentation is not as rich as CiteSpace’s. CiteSpace software has all the functions mentioned above, as well as time slicing technology, which supports more intuitive performance of time series in network analysis for systematic review [36]. CiteSpace is a Java application for structural and temporal analyses of various networks derived from the academic literature and has been optimized several times in recent years to improve its function and practicability [27]. It supports networks with hybrid node types (e.g., institutions and countries), and hybrid link types (e.g., co-citations and co-occurrence) [29,37]. CiteSpace can also detect trends and citation bursts of academic papers by calculating publication indicators [34]. Therefore, version 5.8R3 of the CiteSpace software was chosen as the bibliometric tool to conduct a comprehensive analysis.

3. Results

3.1. Overview of Research

Table 1 and Table 2 illustrate the trends of identified articles regarding the wearable devices in construction safety by country, year, and journal/conference. The data in Table 1 are derived from the article list filtering function in Scopus database. In term of the geographic distribution, the contribution of the United States to the literature of wearable devices in construction safety is the most (N = 88, P = 42.1%). In fact, it is much larger than the second one, i.e., Hong Kong (N = 31, P = 14.8%) and the third one, i.e., Mainland of China (N = 23, P = 11.0%) (see Table 1).
As shown in Table 2, Automation in Construction and Journal of Construction Engineering and Management have published the most articles at 36 (i.e., 21.3%) and 18 (i.e., 10.7%) out of 169 identified articles. The papers published in Automation in Construction are significantly more than those published in other journals. The total number of publications on this topic by year has increased. Until 2016, the number of articles was under 10 per year, but from 2017 to 2021 the number of publications has increased to triple, e.g., 2021 (N = 31). It indicates that the study on applying wearable devices in construction safety has attracted increasing interest of researchers and practitioners.
Table 3 lists the top 10 cited papers of wearable devices in construction safety. Six papers were published in Automation in Construction and two were published in Applied Ergonomics. The remaining two papers were published in Journal of Construction Engineering and Management and Journal of Computing in Civil Engineering. In terms of research content, many of them focus on the construction workers’ posture and activity, including three concerning about work-related musculoskeletal disorders (WMSDs) [23,38,39], and three concentrating on ergonomic analysis, fall detection, and activity recognition [19,40,41]. In these studies, inertial measurement unit (IMU), accelerometer gyroscope, and linear accelerometer, etc., were the most commonly used wearable sensors. The remaining of them focus on workers’ fatigue or stress levels, collecting physiological data from construction workers using heart rate, body surface temperature, and EEG data [18,42].

3.2. Co-Authorship Analysis

Co-authorship analysis can identify main researchers and research communities in this field. Figure 1 depicts the co-authorship network generated from the literature data, and visualized by CiteSpace. The 212 nodes represent the authors in the cited literature, and the 340 links represent their co-authorship relationships. The color of the links represents different ranges of years, e.g., gray, blue, green, yellow, orange, and red, and those colors range from light to dark, corresponding to different years from 2005 to 2021, as shown in Figure 1. A high ‘count’ parameter indicates a great influence of authors in the field. As shown in Figure 1, the larger the ‘count’ parameter, the larger the author’s name label size, e.g., Heng Li (Hong Kong, count = 19), Houtan Jebelli (USA, count = 15), SangHyun Lee (USA, count = 13), Antwi-Afari Maxwell Fordjour (United Kingdom, count = 10), Changbum Ryan Ahn (USA, count = 9), Jiayu Chen (Hong Kong, count = 8), Chukwuma Nnaji (USA, count = 8), Kanghyeok Yang (South Korea, count = 7), Byungjoo Choi (South Korea, count = 7), Ibukun Gabriel Awolusi (USA, count = 7).
When the links form a closed-loop circuit, the linked authors share a strong interaction relationship. such as the circuit of SangHyun Lee, Houtan Jebelli, Yizhi Liu and Mahmoud Habibnezhad. In addition, multiple research communities can be identified through these closed loops and productive authors can be found within them. For example, Heng Li and Antwi-Afari Maxwell Fordjour are the two crucial authors of a research community, including Waleed Umer, Shahnawaz Anwer, Arnold Wong, etc., and Jiayu Chen is the crucial author of a research community, consisting of Di Wang, Dong Zhao, Dai, Fei, etc.
In graph theory, a node with high betweenness centrality usually means that the node is located in a more crucial position in the network. The top five authors with this property were Heng Li (centrality = 0.05), JoonOh Seo (centrality = 0.03) Jiayu Chen (centrality = 0.02), Cenfei Sun (centrality = 0.02), and SangHyun Lee (centrality = 0.01). An author with many counts and a high betweenness centrality in Figure 1 will most likely lead the research field of wearable devices in construction safety. Combined with the above metrics of main researchers and the links of research communities in Figure 1, we can continue to explore the research direction of specific communities and find the most influential articles based on node information.

3.3. Keyword Co-Occurrence Network

Figure 2 shows an overview of the keywords co-occurrence network with 379 nodes generated from the dataset. Each node represents one keyword term specified in the articles. Table 4 lists the top 28 terms (frequency > 10) with a total of 752 co-occurrence frequencies, which account for 47.9% of all keyword frequencies.
According to Figure 2 and Table 4, occupational risk is the most frequent keyword, appearing 77 times, revealing that most studies are inspired by the occupational injuries suffered by workers in the construction industry. The second most frequently mentioned keyword is wearable technology (73 times), showing that wearable technology is the main research focus in this field. Following these two keywords, construction workers, construction industry, wearable sensor and construction safety are also mentioned frequently, with 66, 54, 47 and 40 times, respectively. These terms constitute the background and objectives of the construction safety research domain. Most of the remaining keywords appear less than 40 times. Some of these low-frequency keywords refer to specific wearable technologies, such as electroencephalography (16 times), inertial measurement unit (13 times), and heart (generally refers to heart rate, 12 times). Some keywords explain the method of analyzing data collected by wearable devices, e.g., ergonomics (19 times), machine learning (15 times), and physiological model (11 times). In addition, some keywords have high betweenness centrality, such as construction worker (centrality = 0.17), risk assessment (centrality = 0.17), accident prevention (centrality = 0.15), construction worker (centrality = 0.14), health (centrality = 0.13). These keywords constitute different research topics and are interrelated. The co-occurrences of these keywords report the major research interests of wearable devices in construction safety.

3.4. Cluster Identification

Knowledge domains can be identified and presented as clusters by the bibliometric review method based on information with the relevant articles. CiteSpace extracts the term from the titles, keywords, or abstracts of the literature as text resource, and then the calculation can be carried out after setting parameters such as node type and selection criteria. CiteSpace provides three assessment measures: Latent Semantic Indexing (LSI), Likelihood Ratio Test (LLR), and Mutual Information (MI) index. One of the methods is selected to extract clustering labels from the titles or abstracts of cited references [44]. In this paper, LLR, recommended by the software author, was chosen as the algorithm, which calculates the p-value based on the likelihood ratio or compares it with a critical value to decide whether to reject the null model, thus obtaining the clustering label of the optimal confidence. Figure 3 illustrates a cluster view of the knowledge domains of wearable devices in construction safety, by the loglikelihood ratio (LLR) algorithm. The modularity score of the network is 0.6857. As this score lies between 0.4 and 0.8, the clustering is deemed to be acceptable. The weighted mean silhouette metric measures the average homogeneity of a cluster [45]. When the clustering size is similar, a higher weighted mean silhouette indicates better consistency of the cluster [46]. Therefore, the weighted mean silhouette score of 0.8447 indicates that the consistency of cluster members is enough. The cluster ID ranges from 0 (largest) to 9 (smallest). The size and quality of each cluster are decided by the number of papers assigned to the cluster and the silhouette value of the cluster, respectively. In Table 5, the mean silhouette of each cluster exceeds 0.6, confirming an acceptable level of clustering validity. The hybrid node network is composed of 379 nodes and 1430 links. The 10 major knowledge clusters are attentional failure (#0), brain-computer interface (#1), activity tracking (#2), industrial work safety (#3), corporate clothing (#4), construction site (#5), accelerometer-based activity recognition (#6), intelligent monitoring (#7), building site (#8), and wearable wireless identification (#9). The next section will discuss these clusters in detail.
Figure 4 shows the timeline view of the network. Each horizontal line represents one cluster and the size of each ring represents the centrality of the nodes. The curved lines represent the relationships between the clusters and the authors. Unlike the cluster view in Figure 3, the timeline view in Figure 4 shows the temporal evolution patterns of the 10 clusters. Specifically, Figure 4 reveals that keywords in cluster #1 (brain-computer interface) and cluster #7 (intelligent monitoring) have the longest time range covered, with relevant keywords appearing from 2005 to 2021. In addition, cluster #0 (attentional failure), cluster #3 (industrial work safety), cluster #5 (construction site), cluster #6 (accelerometer-based activity recognition), and cluster #9 (wearable wireless identification) all emerged after 2010. Cluster #4 (corporate clothing) and cluster #5 (construction site) contain some central keywords, making the tree-ring circles in the figure larger. Moreover, it can be found that red links are mostly distributed in the first five clusters (#0–#3), indicating that the research hotspots in the recent five years are concentrated in these clusters.

4. Discussion

Wearable devices in construction safety have focused on technologies and applications. Four clusters—brain–computer interface (#1), accelerometer-based activity recognition (#6), and wearable wireless identification (#9)—are placed into the technology category, which encompasses the basic functions of wearable devices and sensors. Most of the tags in this category possess obvious technical attributes. Accelerometer-based activity recognition, for example, are commonly employed as collectors of worker activity data (e.g., identifying body posture and acceleration, and walking steps) in construction safety. The remaining clusters—attentional failure (#0), activity tracking (#2), corporate clothing (#4), and intelligent monitoring (#7)—are categorized as applications. Most of the works in these clusters employ existing wearable technologies in novel assessment systems of construction risk (e.g., worker pressure, worker falls and collision damage, and other relevant occupational disease risk). In addition, cluster #3 (industrial work safety), cluster #5 (construction site), and cluster #8 (building site) also illustrate the application scenarios of wearable technology. The clustering results effectively identify the emerging research hotspots in this domain.

4.1. Cluster #0 (Attentional Failure)

The most significant cluster is cluster #0 (attentional failure). The construction industry is labor-intensive and necessarily involves repetitive manual labor [81]. Highly physically demanding activities increase the risk of physical fatigue [43], which increases the likelihood of attentional failure and tends to have adverse consequences for construction workers [82].The most common construction accidents are usually related to equipment operation, and attention failure is the leading cause of equipment operator error [83,84]. Using eye-tracking technology, workers’ attention allocation, mental fatigue, and hazard detection abilities can be well evaluated [84,85]. Eye-tracking devices can also be used to measure some metrics of visual search patterns (e.g., fixation count, fixation rate, fixation spatial density, and fixation time [86]) to determine workers’ perception of hazard in empirical investigation [87]. Meanwhile, based on computer vision technology, the data of eye-tracking devices can be uploaded to 3D point cloud to build a training environment, which can further analyze the attention distribution of workers [20]. Besides, Jebelli et al. (2019) reported that the physical state of workers is measurable [17]. As fatigue is mainly related to work intensity, it can also be measured in terms of physical demands [88]. In recent years, obtaining the physical demand levels of workers through physiological signals has followed a common research path. Jebelli et al. (2019) revealed that the physical demand levels and stress states of workers are important considerations in a construction environment, and that physical demand on the construction site can be detected by wearable devices [17]. Aryal et al. (2017) monitored physical fatigue by wearable devices, and subjectively collected the fatigue level by the Borg’s Rate of Perceived Exertion scale [42]. Li and Gerber (2012) non-intrusively evaluated the physiological load of construction workers using wearable sensors, and found that heartrate was sensitive to rest breaks during the construction test [89]. Gatti et al. (2012) related the physical strain measured by wearable devices to the productivity of construction, and identified heartrate as a significant predictor with a strong parabolic relationship to productivity [90].

4.2. Cluster #1 (Brain-Computer Interface)

The second most significant cluster is cluster #1 (brain-computer interface). This cluster label refers to the exchange of information between the brain and the device, and the main way to achieve this in construction safety research community is through wearable electroencephalography (EEG) devices. In the application stage, it has proved feasible to identify workers’ stress status by brain waves. For example, wearable EEG devices can assess the mental workload, attention, and vigilance of workers [78]. EEG captures the electrical activity of firing neurons in the brain [91], and hence the mental statuses (e.g., emotional states) of construction workers [18]. This widely used technique assesses individuals’ stress by analyzing their brain waves [18]. The attention levels of construction workers can also be effectively monitored by wearable EEG systems [92]. EEG rapidly indicates any changes in workers’ mental statuses. However, acquiring high-quality EEG signals is more challenging than collecting other physiological indicators, because the signals are interfered by automatic actions such as eye blinking. Previous studies have also shown that displaying images of construction hazard in a laboratory environment can lead to information distortion, and these images do not have as much impact on the pupil or brain as they do in real life [93]. Therefore, hazard recognition process can be simulated as far as possible by simulating construction hazards site with virtual reality (VR) technology and collecting data through wearable electroencephalogram in VR environment [94]. Jebelli et al. (2019) found that stress is less accurately recognized by EEG than by physiological signals collected by a wristband-type sensor [67]. Additionally, wristband devices can measure their physical demands. Wearable devices equipped with photo plethysmography sensors can monitor a worker’s heart rate [95]. Besides, human-robot collaboration can be achieved through brain-computer interface (BCI) [96]. Liu et al. proposed a BCI based system that can control collaborative construction robots with 90% accuracy using EEG signals [56]. This technology has the potential to improve productivity and help workers to avoid hazardous working conditions.

4.3. Cluster #2 (Activity Tracking) and Cluster #6 (Accelerometer-Based Activity Recognition)

Cluster #2 (activity tracking) and cluster #6 (accelerometer-based activity recognition) represent a similar research topic. For construction workers, lifting, squatting, walking, and even turning screws and swinging tools can be repeated many times. Therefore, the recognition of workers’ movements or behavior patterns is the first step to find the abnormal situation of construction workers. Koskimaki et al. (2009) identified these movements with accelerometer and gyroscope (angular speed) with 88.2% accuracy. The study of Work-related Musculoskeletal Disorders (WMSDs) has been developed by many researchers in recent years on the basis of the identification of worker postures and activities [19,23,38,39]. According to relevant study, falling from heights is among the most common accidents in the construction industry [97], which is strongly associated with loss of balance [21]. Some previous empirical research on falling-risk assessment have shown that wearable inertial measurement units (WIMUs) effectively gather the data of workers’ body responses (such as balance and gait) [12,21,98]. For example, Umer et al. (2018) detected task-induced changes in the static balances of construction workers equipped with WIMUs [99]. In addition, some systems (such as multi-parameter monitoring wearable sensor (MPMWS)) composed of multiple sensors are widely used in analysis of worker’s trunk posture [100]. However, these devices need to be placed in multiple places on the worker’s body, which can cause mobility inconvenience. It is worth noting that some researchers have devoted to developing less invasive wearable measurement devices in recent years. For example, utilizing a wearable insole system with higher accuracy than previous wearable inertial devices to identify falling risk [48,101]. The wearable insole pressure system provides more substantial safety gait metrics than the WIMU system, and extends the current wearable technologies for construction safety [21,48]. In laboratory conditions, built-in sensors of smartphones have been proven to recognize worker’s postures effectively [16,19,102]. According to previous studies, accelerometers are usually placed at the waist or back [38,103,104]. By contrast, wristband-type activity tracker has higher flexibility and lower hardware costs [11]. Therefore, future research is promising to focus on the portability and accuracy of wearable devices.

4.4. Cluster #5 (Construction Site)

It is worth noting that cluster #5 (construction site) has two alternative labels (“wearable biosensor” and “physical demand”). It appears that most of these studies are based on wearable sensors that measure the workers’ physiological states. The measurement and collection of safety data is essential for safety monitoring in the construction industry. As shown in Figure 4, there are three large tree-ring circles in the timeline of cluster #5 (construction site), indicating that keywords in this cluster were widely cited by articles of the construction safety research community. The wearable technologies applied in other sectors can monitor and measure a wide variety of safety performance metrics within this industry [24]. In addition to the EEG devices mentioned in cluster #1, Guo et al. (2017) found that workers’ physical data (heart rate, skin temperature, calorie consumption, etc.) could indirectly measure their psychological status [76]. Pillsbury et al. also effectively assessed the physical and health status of workers by measuring heart rate, respiration rate, and core temperature through physiological status monitors [61]. In addition, upper body posture angle, traveling speed, and acceleration have also been shown to be added to the system of physiological metrics [105]. These case studies have shown the practical effectiveness of safety monitoring based on various physiological indicators collected by wearable biosensor.

4.5. Relationships between Clusters

The remaining clusters represent specific techniques and knowledge domain in construction safety research. For example, cluster #4 (corporate clothing) illustrates the application potential of textile technology in wearable devices, cluster #7 (intelligent monitoring) summarizes the prospect of intelligence and automatic monitoring for the construction safety, and cluster #3 (industrial work safety), cluster #5 (construction site), and cluster #8 (building site) echo the application scenarios of wearable technology in this review. From the above discussion, it can be found that these cluster labels well represent the respective knowledge domain. In addition, different research directions may use the same wearable devices, which means that the database of construction safety field has the potential to be established. At the same time, further development of wearable technology in the future will constantly open up new application scenarios for this field.

5. Conclusions

This paper provides an objective and accurate bibliometric analysis of wearable applications in the field of construction safety. The analysis was based on selected papers published between 2005 and 2021. Many key areas were identified by keyword co-occurrence analysis, such as ergonomics, electroencephalography, and inertial measurement unit. Ten knowledge clusters were identified: attentional failure, brain-computer interface, activity tracking, industrial work safety, corporate clothing, construction site, accelerometer-based activity recognition, intelligent monitoring, building site, and wearable wireless identification.
Through this systematic and quantitative bibliometric analysis, we could clearly visualize and explain the knowledge clusters and the frontier of wearable devices in construction safety. The present work highlights the developments and trends in this research domain and provides a clear perspective based on comprehensive data and statistical analysis. The developments have been clearly summarized by information maps and statistical descriptions. In future work, the performance of wearable devices should be further improved to reduce monitoring bias and to create low-cost systems with potential for commercial promotion. Future construction safety might also employ integrated wearable sensors for multi-parameter monitoring. In fact, to design an integrated multi-functional wearable system is another developmental trend. It is worth noting that some wearable technologies have been available for other industries for years, but have only recently been applied to construction safety. Further research could focus on whether mature equipment from other industries can be adapted to scenarios in the field of construction safety.
Although the relevant literature has been carefully collected and analyzed, this research has several limitations. Although this paper screened literatures from the Scopus database and the Web of Science database, a manual review would inevitably be subjective. At the same time, due to the limitation of the software algorithm, the discussion part is based on the 10 clusters identified, which may result in the omission of some relevant knowledge fields. Significant contributions could be ignored as a result of this deficient coverage. In addition, some literature might be ignored when using keywords to search for literature. Therefore, the research results could not completely cover the entire literature related to wearable devices in construction safety. Future studies should address the limitations by utilizing various databases and broadening data sources to collect and review literature.

Author Contributions

Conceptualization, R.G.; methodology, R.G. and S.L.; software, B.M.; validation, H.W., S.L. and C.Y.; writing—original draft preparation, B.M. and R.G.; writing—review and editing, R.G., H.W., S.L. and C.Y.; visualization, B.M.; supervision, R.G.; funding acquisition, R.G., H.W. and C.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Humanities and Social Science Fund of the Ministry of Education of China, grant number 20YJC630023, 19YJCZH154 and 21YJAZH104; National Natural Science Foundation of China, grant number 72074240 and 72174220; Program for Innovation Research in Central University of Finance and Economics; First Class Academic Discipline Construction Project of Central University of Finance and Economics, grant number 20190807-8.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors gratefully acknowledge the School of Management Science and Engineering at Central University of Finance and Economics for providing technical support to conduct this research. The authors also acknowledge the anonymous reviewers for their comments.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ringen, K.; Seegal, J.; England, A. Safety and health in the construction industry. Annu. Rev. Public Health 1995, 16, 165–188. [Google Scholar] [CrossRef] [PubMed]
  2. Im, H.-J.; Kwon, Y.-J.; Kim, S.-G.; Kim, Y.-K.; Ju, Y.-S.; Lee, H.-P. The characteristics of fatal occupational injuries in Korea’s construction industry, 1997–2004. Saf. Sci. 2009, 47, 1159–1162. [Google Scholar] [CrossRef]
  3. Sunindijo, R.; Zou, P. How project manager’s skills may influence the development of safety climate in construction projects. Int. J. Proj. Organ. Manag. 2012, 4, 286–301. [Google Scholar] [CrossRef]
  4. Suraji, A.; Duff, A.R.; Peckitt, S.J. Development of Causal Model of Construction Accident Causation. J. Constr. Eng. Manag. 2001, 127, 337–344. [Google Scholar] [CrossRef]
  5. Demirkesen, S.; Arditi, D. Construction safety personnel’s perceptions of safety training practices. Int. J. Proj. Manag. 2015, 33, 1160–1169. [Google Scholar] [CrossRef]
  6. Ahn, C.R.; Lee, S.; Sun, C.; Jebelli, H.; Yang, K.; Choi, B. Wearable Sensing Technology Applications in Construction Safety and Health. J. Constr. Eng. Manag. 2019, 145, 03119007. [Google Scholar] [CrossRef]
  7. Hammad, A.; Khabeer, B.; Mozaffari, E.; Devarakonda, P.; Bauchkar, P. Augmented reality interaction model for mobile infrastructure management systems. In Proceedings of the 33rd Annual Conference of the Canadian Society for Civil Engineering 2005, Toronto, ON, Canada, 2–4 June 2005. [Google Scholar]
  8. Korman, D.B.; Zulps, A. Enhancing construction safety using wearable technology. In Proceedings of the ASSE Professional Development Conference and Exposition, Denver, CO, USA, 19–22 June 2017; p. ASSE-17-552. [Google Scholar]
  9. Navon, R. Automated project performance control of construction projects. Autom. Constr. 2005, 14, 467–476. [Google Scholar] [CrossRef]
  10. Lee, W.; Lin, K.-Y.; Seto, E.; Migliaccio, G. Wearable sensors for monitoring on-duty and off-duty worker physiological status and activities in construction. Autom. Constr. 2017, 83, 341–353. [Google Scholar] [CrossRef]
  11. Ryu, J.; Seo, J.; Jebelli, H.; Lee, S. Automated Action Recognition Using an Accelerometer-Embedded Wristband-Type Activity Tracker. J. Constr. Eng. Manag. 2019, 145, 04018114. [Google Scholar] [CrossRef]
  12. Yang, K.; Ahn, C.R.; Vuran, M.C.; Kim, H. Collective sensing of workers’ gait patterns to identify fall hazards in construction. Autom. Constr. 2017, 82, 166–178. [Google Scholar] [CrossRef]
  13. Kosuda, T.; Nakajo, Y.; Sasagawa, K.; Nishikai, Y.; Shimizu, S.; Kumita, Y.; Kondo, T.; Hashimoto, N. Development of a helmet-type wearable device capable of measuring perspiration during various activities. In Proceedings of the 2019 International Conference on Electronics Packaging (ICEP), Niigata, Japan, 17–20 April 2019. [Google Scholar] [CrossRef]
  14. Park, J.; Kim, K.; Cho, Y.K. Framework of Automated Construction-Safety Monitoring Using Cloud-Enabled BIM and BLE Mobile Tracking Sensors. J. Constr. Eng. Manag. 2017, 143, 05016019. [Google Scholar] [CrossRef]
  15. Cheng, T.; Teizer, J. Real-time resource location data collection and visualization technology for construction safety and activity monitoring applications. Autom. Constr. 2013, 34, 3–15. [Google Scholar] [CrossRef]
  16. Nath, N.D.; Chaspari, T.; Behzadan, A.H. Automated ergonomic risk monitoring using body-mounted sensors and machine learning. Adv. Eng. Inform. 2018, 38, 514–526. [Google Scholar] [CrossRef]
  17. Jebelli, H.; Choi, B.; Lee, S. Application of Wearable Biosensors to Construction Sites. II: Assessing Workers’ Physical Demand. J. Constr. Eng. Manag. 2019, 145, 04019080. [Google Scholar] [CrossRef]
  18. Jebelli, H.; Hwang, S.; Lee, S. EEG-based workers’ stress recognition at construction sites. Autom. Constr. 2018, 93, 315–324. [Google Scholar] [CrossRef]
  19. Nath, N.D.; Akhavian, R.; Behzadan, A.H. Ergonomic analysis of construction worker’s body postures using wearable mobile sensors. Appl. Ergon. 2017, 62, 107–117. [Google Scholar] [CrossRef] [Green Version]
  20. Jeelani, I.; Han, K.; Albert, A. Automating and scaling personalized safety training using eye-tracking data. Autom. Constr. 2018, 93, 63–77. [Google Scholar] [CrossRef]
  21. Antwi-Afari, M.F.; Li, H. Fall risk assessment of construction workers based on biomechanical gait stability parameters using wearable insole pressure system. Adv. Eng. Inform. 2018, 38, 683–694. [Google Scholar] [CrossRef]
  22. Teizer, J. Wearable, wireless identification sensing platform: Self-Monitoring Alert and Reporting Technology for Hazard Avoidance and Training (SmartHat). J. Inf. Technol. Construct. 2015, 20, 295–312. [Google Scholar]
  23. Wang, D.; Dai, F.; Ning, X. Risk Assessment of Work-Related Musculoskeletal Disorders in Construction: State-of-the-Art Review. J. Constr. Eng. Manag. 2015, 141, 04015008. [Google Scholar] [CrossRef]
  24. Awolusi, I.; Marks, E.; Hallowell, M. Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices. Autom. Constr. 2018, 85, 96–106. [Google Scholar] [CrossRef]
  25. Pritchard, A. Statistical bibliography or bibliometrics. J. Doc. 1969, 25, 348–349. [Google Scholar]
  26. Mayr, P.; Scharnhorst, A. Scientometrics and information retrieval: Weak-links revitalized. Scientometrics 2014, 102, 2193–2199. [Google Scholar] [CrossRef] [Green Version]
  27. Saheb, T.; Izadi, L. Paradigm of IoT big data analytics in the healthcare industry: A review of scientific literature and mapping of research trends. Telemat. Inform. 2019, 41, 70–85. [Google Scholar] [CrossRef]
  28. Li, X.; Wu, W.; Shen, Q.; Wang, X.; Teng, Y. Mapping the knowledge domains of Building Information Modeling (BIM): A bibliometric approach. Autom. Constr. 2017, 84, 195–206. [Google Scholar] [CrossRef]
  29. Chen, D.; Liu, Z.; Luo, Z.; Webber, M.; Chen, J. Bibliometric and visualized analysis of emergy research. Ecol. Eng. 2016, 90, 285–293. [Google Scholar] [CrossRef]
  30. Small, H. Co-citation in the scientific literature: A new measure of the relationship between two documents. J. Am. Soc. Inf. Sci. 1973, 24, 265–269. [Google Scholar] [CrossRef]
  31. Bayer, A.E.; Smart, J.C.; McLaughlin, G.W. Mapping intellectual structure of a scientific subfield through author cocitations: Introduction. J. Am. Soc. Inf. Sci. (1986–1998) 1990, 41, 444. [Google Scholar] [CrossRef]
  32. Su, H.-N.; Lee, P.-C. Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in Technology Foresight. Scientometrics 2010, 85, 65–79. [Google Scholar] [CrossRef]
  33. Chen, C.; Morris, S. Visualizing evolving networks: Minimum spanning trees versus pathfinder networks. In Proceedings of the IEEE Symposium on Information Visualization 2003, Seattle, WA, USA, 19–21 October 2003. [Google Scholar]
  34. Chen, C.; Hu, Z.; Liu, S.; Tseng, H. Emerging trends in regenerative medicine: A scientometric analysis in CiteSpace. Expert Opin. Biol. Ther. 2012, 12, 593–608. [Google Scholar] [CrossRef]
  35. Kam, M.K.; Leung, S.-F.; Zee, B.C.-Y.; Chau, R.M.; Suen, J.J.; Mo, F.; Lai, M.; Ho, R.; Cheung, K.-Y.; Yu, B.K.; et al. Prospective Randomized Study of Intensity-Modulated Radiotherapy on Salivary Gland Function in Early-Stage Nasopharyngeal Carcinoma Patients. J. Clin. Oncol. 2007, 25, 4873–4879. [Google Scholar] [CrossRef] [PubMed]
  36. Chen, C. Science Mapping: A Systematic Review of the Literature. J. Data Inf. Sci. 2017, 2, 1–40. [Google Scholar] [CrossRef] [Green Version]
  37. Chen, C. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inf. Sci. Technol. 2006, 57, 359–377. [Google Scholar] [CrossRef] [Green Version]
  38. Yan, X.; Li, H.; Li, A.R.; Zhang, H. Wearable IMU-based real-time motion warning system for construction workers’ musculoskeletal disorders prevention. Autom. Constr. 2017, 74, 2–11. [Google Scholar] [CrossRef]
  39. Valero, E.; Sivanathan, A.; Bosché, F.; Abdel-Wahab, M. Musculoskeletal disorders in construction: A review and a novel system for activity tracking with body area network. Appl. Ergon. 2016, 54, 120–130. [Google Scholar] [CrossRef] [PubMed]
  40. Yang, K.; Ahn, C.R.; Vuran, M.C.; Aria, S.S. Semi-supervised near-miss fall detection for ironworkers with a wearable inertial measurement unit. Autom. Constr. 2016, 68, 194–202. [Google Scholar] [CrossRef] [Green Version]
  41. Joshua, L.; Varghese, K. Accelerometer-Based Activity Recognition in Construction. J. Comput. Civ. Eng. 2011, 25, 370–379. [Google Scholar] [CrossRef]
  42. Aryal, A.; Ghahramani, A.; Becerik-Gerber, B. Monitoring fatigue in construction workers using physiological measurements. Autom. Constr. 2017, 82, 154–165. [Google Scholar] [CrossRef]
  43. Choi, B.; Hwang, S.; Lee, S.H. What drives construction workers’ acceptance of wearable technologies in the workplace?: Indoor localization and wearable health devices for occupational safety and health. Autom. Constr. 2017, 84, 31–41. [Google Scholar] [CrossRef]
  44. Chen, C. The citespace manual. Coll. Comput. Inf. 2014, 1, 1–84. [Google Scholar]
  45. Rousseeuw, P.J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 1987, 20, 53–65. [Google Scholar] [CrossRef] [Green Version]
  46. Chen, C. The CiteSpace Manual. Available online: httphttp://cluster.ischool.drexel.edu/~cchen/citespace/manual/CiteSpaceChinese.pdf (accessed on 8 March 2022).
  47. Hashiguchi, N.; Yeongjoo, L.; Sya, C.; Kuroishi, S.; Miyazaki, Y.; Kitahara, S.; Kobayashi, T.; Tateyama, K.; Kodama, K. Real-time Judgment of Workload using Heart Rate and Physical Activity. In From Demonstration to Practical Use—To New Stage of Construction Robot: Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC 2020), Kitakyushu, Japan, 27–28 October 2020; International Association on Automation and Robotics in Construction (IAARC): Waterloo, ON, Canada, 2020; pp. 849–856. [Google Scholar] [CrossRef]
  48. Antwi-Afari, M.F.; Li, H.; Anwer, S.; Yevu, S.K.; Wu, Z.; Antwi-Afari, P.; Kim, I. Quantifying workers’ gait patterns to identify safety hazards in construction using a wearable insole pressure system. Saf. Sci. 2020, 129, 104855. [Google Scholar] [CrossRef]
  49. Jeelani, I.; Han, K.; Albert, A. Scaling Personalized Safety Training Using Automated Feedback Generation. In Construction Research Congress 2018: Safety and Disaster Management; American Society of Civil Engineers: Atlanta, GA, USA, 2018; pp. 196–206. [Google Scholar]
  50. Huanga, Y.; Le, T. Factors affecting the implementation of ai-based hearing protection technology at construction workplace. In From Demonstration to Practical Use—To New Stage of Construction Robot: Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC 2020), Kitakyushu, Japan, 27–28 October 2020; International Association on Automation and Robotics in Construction (IAARC): Waterloo, ON, Canada, 2020; Volume 37, pp. 1014–1020. [Google Scholar]
  51. Hasanzadeh, S.; Dao, B.; Esmaeili, B.; Dodd, M.D. Role of Personality in Construction Safety: Investigating the Relationships between Personality, Attentional Failure, and Hazard Identification under Fall-Hazard Conditions. J. Constr. Eng. Manag. 2019, 145, 04019052. [Google Scholar] [CrossRef] [Green Version]
  52. Jebelli, H.; Khalili, M.M.; Hwang, S.; Lee, S. A Supervised Learning-Based Construction Workers’ Stress Recognition Using a Wearable Electroencephalography (EEG) Device. In Construction Research Congress 2018: Safety and Disaster Management; American Society of Civil Engineers: Atlanta, GA, USA, 2018; pp. 43–53. [Google Scholar]
  53. Anwer, S.; Li, H.; Antwi-Afari, M.F.; Umer, W.; Mehmood, I.; Wong, A.Y.L. Effects of load carrying techniques on gait parameters, dynamic balance, and physiological parameters during a manual material handling task. Eng. Constr. Arch. Manag. 2021. [Google Scholar] [CrossRef]
  54. Umer, W.; Li, H.; Yantao, Y.; Antwi-Afari, M.F.; Anwer, S.; Luo, X. Physical exertion modeling for construction tasks using combined cardiorespiratory and thermoregulatory measures. Autom. Constr. 2020, 112, 103079. [Google Scholar] [CrossRef]
  55. Zuluaga, C.M.; Albert, A.; Winkel, M.A. Improving Safety, Efficiency, and Productivity: Evaluation of Fall Protection Systems for Bridge Work Using Wearable Technology and Utility Analysis. J. Constr. Eng. Manag. 2020, 146, 04019107. [Google Scholar] [CrossRef]
  56. Liu, Y.; Habibnezhad, M.; Jebelli, H. Brain-computer interface for hands-free teleoperation of construction robots. Autom. Constr. 2021, 123, 103523. [Google Scholar] [CrossRef]
  57. Hashiguchi, N.; Kodama, K.; Lim, Y.; Che, C.; Kuroishi, S.; Miyazaki, Y.; Kobayashi, T.; Kitahara, S.; Tateyama, K. Practical Judgment of Workload Based on Physical Activity, Work Conditions, and Worker’s Age in Construction Site. Sensors 2020, 20, 3786. [Google Scholar] [CrossRef]
  58. Huang, C.; Kim, W.; Zhang, Y.; Xiong, S. Development and Validation of a Wearable Inertial Sensors-Based Automated System for Assessing Work-Related Musculoskeletal Disorders in the Workspace. Int. J. Environ. Res. Public Health 2020, 17, 6050. [Google Scholar] [CrossRef]
  59. Svertoka, E.; Saafi, S.; Rusu-Casandra, A.; Burget, R.; Marghescu, I.; Hosek, J.; Ometov, A. Wearables for Industrial Work Safety: A Survey. Sensors 2021, 21, 3844. [Google Scholar] [CrossRef]
  60. Shakerian, S.; Habibnezhad, M.; Ojha, A.; Lee, G.; Liu, Y.; Jebelli, H.; Lee, S. Assessing occupational risk of heat stress at construction: A worker-centric wearable sensor-based approach. Saf. Sci. 2021, 142, 105395. [Google Scholar] [CrossRef]
  61. Pillsbury, W.; Clevenger, C.M.; Abdallah, M.; Young, R. Capabilities of an Assessment System for Construction Worker Physiology. J. Perform. Constr. Facil. 2020, 34, 04019120. [Google Scholar] [CrossRef]
  62. Nnaji, C.; Awolusi, I. Critical success factors influencing wearable sensing device implementation in AEC industry. Technol. Soc. 2021, 66, 101636. [Google Scholar] [CrossRef]
  63. Holme, I. Personal protection, corporate clothing and workwear debated. Tech. Text. Int. 2007, 16, 39–45. [Google Scholar]
  64. Sivanathan, A.; Abdel-Wahab, M.; Bosche, F.; Lim, T. Towards a Cyber-Physical Gaming System for Training in the Construction and Engineering Industry. In Proceedings of the ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Buffalo, NY, USA, 17–20 August 2014. [Google Scholar] [CrossRef]
  65. Lee, W.; Migliaccio, G.C.; Lin, K.-Y.; Seto, E.Y. Workforce development: Understanding task-level job demands-resources, burnout, and performance in unskilled construction workers. Saf. Sci. 2020, 123, 104577. [Google Scholar] [CrossRef]
  66. Conforti, I.; Mileti, I.; Del Prete, Z.; Palermo, E. Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach. Sensors 2020, 20, 1557. [Google Scholar] [CrossRef] [Green Version]
  67. Jebelli, H.; Choi, B.; Lee, S. Application of Wearable Biosensors to Construction Sites. I: Assessing Workers’ Stress. J. Constr. Eng. Manag. 2019, 145, 04019079. [Google Scholar] [CrossRef]
  68. Sugimoto, M.; Hamasaki, S.; Yajima, R.; Yamakawa, H.; Takakusaki, K.; Nagatani, K.; Yamashita, A.; Asama, H. Incident Detection at Construction Sites via Heart-Rate and EMG Signal of Facial Muscle. In From Demonstration to Practical Use—To New Stage of Construction Robot: Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC 2020), Kitakyushu, Japan, 27–28 October 2020; International Association on Automation and Robotics in Construction (IAARC): Waterloo, ON, Canada, 2020; pp. 886–891. [Google Scholar]
  69. Sakhakarmi, S.; Park, J. Wearable Tactile System for Improved Hazard Perception in Construction Sites. In Construction Research Congress 2020: Safety, Workforce, and Education; American Society of Civil Engineers: Atlanta, GA, USA, 2020; pp. 120–128. [Google Scholar]
  70. Antwi-Afari, M.F.; Li, H.; Umer, W.; Yu, Y.; Xing, X. Construction Activity Recognition and Ergonomic Risk Assessment Using a Wearable Insole Pressure System. J. Constr. Eng. Manag. 2020, 146, 04020077. [Google Scholar] [CrossRef]
  71. Sanhudo, L.; Calvetti, D.; Martins, J.P.; Ramos, N.M.; Mêda, P.; Gonçalves, M.C.; Sousa, H. Activity classification using accelerometers and machine learning for complex construction worker activities. J. Build. Eng. 2020, 35, 102001. [Google Scholar] [CrossRef]
  72. Barro-Torres, S.; Fernández-Caramés, T.M.; Pérez-Iglesias, H.J.; Escudero, C.J. Real-time personal protective equipment monitoring system. Comput. Commun. 2012, 36, 42–50. [Google Scholar] [CrossRef]
  73. Forsyth, J.B.; Martin, T.L.; Young-Corbett, D.; Dorsa, E. Feasibility of Intelligent Monitoring of Construction Workers for Carbon Monoxide Poisoning. IEEE Trans. Autom. Sci. Eng. 2012, 9, 505–515. [Google Scholar] [CrossRef]
  74. Anwer, S.; Li, H.; Antwi-Afari, M.; Umer, W.; Wong, A. Cardiorespiratory and Thermoregulatory Parameters Are Good Surrogates for Measuring Physical Fatigue during a Simulated Construction Task. Int. J. Environ. Res. Public Health 2020, 17, 5418. [Google Scholar] [CrossRef] [PubMed]
  75. Sassi, A.; Gioanola, L.; Civera, P. Proposal of a workers and scaffolds monitoring and risk mitigation system for building sites. In Bridge Maintenance, Safety, Management and Life-Cycle Optimization: Proceedings of the Fifth International IABMAS Conference, Philadelphia, USA, 11–15 July 2010; CRC Press: Boca Raton, FL, USA, 2010; Volume 20100869, p. 329. [Google Scholar]
  76. Guo, H.; Yu, Y.; Xiang, T.; Li, H.; Zhang, D. The availability of wearable-device-based physical data for the measurement of construction workers’ psychological status on site: From the perspective of safety management. Autom. Constr. 2017, 82, 207–217. [Google Scholar] [CrossRef]
  77. Bangaru, S.S.; Wang, C.; Aghazadeh, F. Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition. Sensors 2020, 20, 5264. [Google Scholar] [CrossRef]
  78. Chen, J.; Song, X.; Lin, Z. Revealing the “Invisible Gorilla” in construction: Estimating construction safety through mental workload assessment. Autom. Constr. 2016, 63, 173–183. [Google Scholar] [CrossRef]
  79. Yu, Y.; Li, H.; Yang, X.; Kong, L.; Luo, X.; Wong, A.Y.L. An automatic and non-invasive physical fatigue assessment method for construction workers. Autom. Constr. 2019, 103, 1–12. [Google Scholar] [CrossRef]
  80. Mori, A.; Asaine, W. Preventing accidents on building construction sites: In case of going up and down the scaffolding steps. J. Struct. Constr. Eng. 2011, 76, 1213–1219. [Google Scholar] [CrossRef] [Green Version]
  81. Ng, S.T.; Tang, Z. Labour-intensive construction sub-contractors: Their critical success factors. Int. J. Proj. Manag. 2010, 28, 732–740. [Google Scholar] [CrossRef]
  82. Sluiter, J.K. High-demand jobs: Age-related diversity in work ability? Appl. Ergon. 2006, 37, 429–440. [Google Scholar] [CrossRef]
  83. Hasanzadeh, S.; Esmaeili, B.; Dodd, M.D. Impact of Construction Workers’ Hazard Identification Skills on Their Visual Attention. J. Constr. Eng. Manag. 2017, 143, 04017070. [Google Scholar] [CrossRef] [Green Version]
  84. Li, J.; Li, H.; Wang, H.; Umer, W.; Fu, H.; Xing, X. Evaluating the impact of mental fatigue on construction equipment operators’ ability to detect hazards using wearable eye-tracking technology. Autom. Constr. 2019, 105, 102835. [Google Scholar] [CrossRef]
  85. Jeelani, I.; Albert, A.; Han, K.; Azevedo, R. Are Visual Search Patterns Predictive of Hazard Recognition Performance? Empirical Investigation Using Eye-Tracking Technology. J. Constr. Eng. Manag. 2019, 145, 04018115. [Google Scholar] [CrossRef]
  86. Sharafi, Z.; Shaffer, T.; Sharif, B.; Gueheneuc, Y.-G. Eye-Tracking Metrics in Software Engineering. In Proceedings of the 2015 Asia-Pacific Software Engineering Conference (APSEC), New Delhi, India, 1–4 December 2015; pp. 96–103. [Google Scholar]
  87. Jeelani, I.; Albert, A.; Han, K. Improving Safety Performance in Construction Using Eye-Tracking, Visual Data Analytics, and Virtual Reality. In Construction Research Congress 2020: Safety, Workforce, and Education; American Society of Civil Engineers: Atlanta, GA, USA, 2020; pp. 395–404. [Google Scholar]
  88. Frone, M.R.; Tidwell, M.-C.O. The meaning and measurement of work fatigue: Development and evaluation of the Three-Dimensional Work Fatigue Inventory (3D-WFI). J. Occup. Health Psychol. 2015, 20, 273–288. [Google Scholar] [CrossRef]
  89. Li, S.; Gerber, B.B. Evaluating Physiological Load of Workers with Wearable Sensors. In Proceedings of the 2012 ASCE International Conference on Computing in Civil Engineering, Clearwater Beach, FL, USA, 17–20 June 2012; pp. 405–412. [Google Scholar]
  90. Gatti, U.C.; Migliaccio, G.C.; Bogus, S.M.; Schneider, S. Using Wearable Physiological Status Monitors for Analyzing the Physical Strain-Productivity Relationship for Construction Tasks. In Proceedings of the 2012 ASCE International Conference on Computing in Civil Engineering, Clearwater Beach, FL, USA, 17–20 June 2012; pp. 577–585. [Google Scholar]
  91. Jovanov, E.; Lords, A.O.; Raskovic, D.; Cox, P.; Adhami, R.; Andrasik, F. Stress monitoring using a distributed wireless intelligent sensor system. IEEE Comput. Graph. Appl. 2003, 22, 49–55. [Google Scholar] [CrossRef] [Green Version]
  92. Yan, X.; Li, H.; Wang, C.; Seo, J.; Zhang, H.; Wang, H. Development of ergonomic posture recognition technique based on 2D ordinary camera for construction hazard prevention through view-invariant features in 2D skeleton motion. Adv. Eng. Inform. 2017, 34, 152–163. [Google Scholar] [CrossRef]
  93. Liao, P.-C.; Sun, X.; Zhang, D. A multimodal study to measure the cognitive demands of hazard recognition in construction workplaces. Saf. Sci. 2021, 133, 105010. [Google Scholar] [CrossRef]
  94. Jeon, J.; Cai, H. Classification of construction hazard-related perceptions using: Wearable electroencephalogram and virtual reality. Autom. Constr. 2021, 132, 103975. [Google Scholar] [CrossRef]
  95. Hwang, S.; Seo, J.; Jebelli, H.; Lee, S. Feasibility analysis of heart rate monitoring of construction workers using a photoplethysmography (PPG) sensor embedded in a wristband-type activity tracker. Autom. Constr. 2016, 71, 372–381. [Google Scholar] [CrossRef]
  96. Liu, Y.; Habibnezhad, M.; Jebelli, H. Brainwave-driven human-robot collaboration in construction. Autom. Constr. 2021, 124, 103556. [Google Scholar] [CrossRef]
  97. Nenonen, N. Analysing factors related to slipping, stumbling, and falling accidents at work: Application of data mining methods to Finnish occupational accidents and diseases statistics database. Appl. Ergon. 2013, 44, 215–224. [Google Scholar] [CrossRef]
  98. Kim, H.; Ahn, C.; Yang, K. Identifying Safety Hazards Using Collective Bodily Responses of Workers. J. Constr. Eng. Manag. 2017, 143, 04016090. [Google Scholar] [CrossRef]
  99. Umer, W.; Li, H.; Lu, W.; Szeto, G.P.Y.; Wong, A.Y. Development of a tool to monitor static balance of construction workers for proactive fall safety management. Autom. Constr. 2018, 94, 438–448. [Google Scholar] [CrossRef]
  100. Lee, W.; Seto, E.; Lin, K.Y.; Migliaccio, G.C. An evaluation of wearable sensors and their placements for analyzing construction worker’s trunk posture in laboratory conditions. Appl. Ergon. 2017, 65, 424–436. [Google Scholar] [CrossRef]
  101. Wang, C.; Kim, Y.; Lee, S.H.; Sung, N.J.; Min, S.D.; Choi, M.H. Activity and safety recognition using smart work shoes for construction worksite. KSII Trans. Int. Inf. Syst. (TIIS) 2020, 14, 654–670. [Google Scholar]
  102. Akhavian, R.; Behzadan, A.H. Smartphone-based construction workers’ activity recognition and classification. Autom. Constr. 2016, 71, 198–209. [Google Scholar] [CrossRef]
  103. Valero, E.; Sivanathan, A.; Bosché, F.; Abdel-Wahab, M. Analysis of construction trade worker body motions using a wearable and wireless motion sensor network. Autom. Constr. 2017, 83, 48–55. [Google Scholar] [CrossRef]
  104. Yang, K.; Aria, S.; Ahn, C.R.; Stentz, T.L. Automated Detection of Near-miss Fall Incidents in Iron Workers Using Inertial Measurement Units. In Construction Research Congress 2014: Construction in a Global Network; American Society of Civil Engineers: Atlanta, GA, USA, 2014; pp. 935–944. [Google Scholar]
  105. Shen, X.; Awolusi, I.; Marks, E. Construction Equipment Operator Physiological Data Assessment and Tracking. Pr. Period. Struct. Des. Constr. 2017, 22, 04017006. [Google Scholar] [CrossRef]
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Figure 1. Co-authorship network of wearable devices in construction safety.
Figure 1. Co-authorship network of wearable devices in construction safety.
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Figure 2. Keyword co-occurrence network of wearable devices in construction safety.
Figure 2. Keyword co-occurrence network of wearable devices in construction safety.
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Figure 3. Cluster view of knowledge domains for wearable devices in construction safety.
Figure 3. Cluster view of knowledge domains for wearable devices in construction safety.
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Figure 4. Timeline view of the co-occurrence network cluster of keywords.
Figure 4. Timeline view of the co-occurrence network cluster of keywords.
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Table
Table 1. Main research origin of papers published.
Table 1. Main research origin of papers published.
CountryInstitute/
University		Researchers
Involved		Number of PapersPercentage Contribution
United States731358842.1%
Hong Kong28543114.8%
China36722311.0%
South Korea25482210.5%
United Kingdom1628104.8%
Australia223694.3%
Japan224083.8%
Italy62652.4%
Saudi Arabia31052.4%
Germany71541.9%
Canada101641.9%
Countries or regions with four or more papers are counted in the table.
Table
Table 2. The publishing year of journals/conference proceedings contributing to the area of wearable devices in construction safety.
Table 2. The publishing year of journals/conference proceedings contributing to the area of wearable devices in construction safety.
SourcePublication Year
2005–20102011201220142015201620172018201920202021Total
Automation in Construction 310763736
Journal of Construction Engineering and Management 1 265418
Sensors (Switzerland) 1528
Advanced Engineering Informatics 1211 5
Congress on Computing in Civil Engineering, Proceedings 12 1 4
Engineering, Construction and Architectural Management 224
Safety Science 1214
Construction Research Congress 2020: Safety, Workforce, and Education—Selected Papers from the Construction Research Congress 2020 4 4
Others62433661110201586
Total6363491822254231169
Journals and conference proceedings with less than four paper were classified into Others.
Table
Table 3. Top 10 cited articles on wearable devices in construction safety.
Table 3. Top 10 cited articles on wearable devices in construction safety.
RankAuthorsTitleCited Frequency JournalRefs.
1Awolusi et al. (2018)Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices 143Automation in Construction[24]
2Yan et al. (2017)Wearable IMU-based real-time motion warning system for construction workers’ musculoskeletal disorders prevention135Automation in Construction[38]
3Aryal et al. (2017)Monitoring fatigue in construction workers using physiological measurements111Automation in Construction[42]
4Valero et al. (2016)Musculoskeletal disorders in construction: A review and a novel system for activity tracking with body area network111Applied Ergonomics[39]
5Wang et al. (2015)Risk assessment of work-related musculoskeletal disorders in construction: State-of-the-art review109Journal of Construction Engineering and Management[23]
6Jebelli et al. (2018)EEG-based workers’ stress recognition at construction sites101Automation in Construction[18]
7Nath et al. (2017)Ergonomic analysis of construction worker’s body postures using wearable mobile sensors94Applied Ergonomics[19]
8Yang et al. (2016)Semi-supervised near-miss fall detection for ironworkers with a wearable inertial measurement unit92Automation in Construction[40]
9Joshua et al. (2011)Accelerometer-based activity recognition in construction86Journal of Computing in Civil Engineering[41]
10Choi et al. (2017)What drives construction workers’ acceptance of wearable technologies in the workplace?: Indoor localization and wearable health devices for occupational safety and health85Automation in Construction[43]
Table
Table 4. Occurrence frequencies of specified keywords in the literature of wearable devices in construction safety.
Table 4. Occurrence frequencies of specified keywords in the literature of wearable devices in construction safety.
KeywordsFrequencyKeywordsFrequency
Occupational risk77Health risk18
Wearable technology73Accident17
Construction worker66Physiology17
Construction industry54Electroencephalography16
Wearable sensor47Machine learning15
Construction safety40Safety engineering13
Accident prevention36Inertial measurement unit13
Risk assessment30Monitoring12
Human resource management29Heart12
Construction site27Survey12
Hazard26Wearable device12
Health19Safety11
Human19Physiological model11
Ergonomics19Productivity11
Table
Table 5. Top-ranked clusters and their main terms.
Table 5. Top-ranked clusters and their main terms.
Cluster IDSizeSilhouetteMean YearCluster Label (LLR)Alternative LabelRepresentative Articles
0470.7312017Attentional failureFall-hazard condition; hazard identification[47,48,49,50,51]
1380.6722017Brain-computer interfaceQuantitative framework; construction safety management[18,52,53,54,55,56]
2340.8572017Activity trackingBody area network; novel system[19,21,39,57,58]
3330.7702019Industrial work safetyHealth risk mitigation; ann-based automated scaffold builder activity recognition[57,59,60,61,62]
4330.9632011Corporate clothingEngineering industry; cyber-physical gaming system;[58,63,64,65,66]
5320.8912014Construction siteWearable biosensor; physical demand[17,18,67,68,69]
6310.7792015Accelerometer-based activity recognitionUsing body-mounted sensor; automated ergonomic risk monitoring[11,16,41,70,71]
7300.9292012Intelligent monitoringCarbon monoxide poisoning; gait pattern[48,58,72,73,74]
8240.8552012Building siteRisk mitigation system; scaffolds monitoring[57,71,75,76,77]
9210.9792014Wearable wireless identificationSensing platform; self-monitoring alert[22,61,78,79,80]
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Gao, R.; Mu, B.; Lyu, S.; Wang, H.; Yi, C. Review of the Application of Wearable Devices in Construction Safety: A Bibliometric Analysis from 2005 to 2021. Buildings 2022, 12, 344. https://doi.org/10.3390/buildings12030344

AMA Style

Gao R, Mu B, Lyu S, Wang H, Yi C. Review of the Application of Wearable Devices in Construction Safety: A Bibliometric Analysis from 2005 to 2021. Buildings. 2022; 12(3):344. https://doi.org/10.3390/buildings12030344

Chicago/Turabian Style

Gao, Ran, Bowen Mu, Sainan Lyu, Hao Wang, and Chengdong Yi. 2022. "Review of the Application of Wearable Devices in Construction Safety: A Bibliometric Analysis from 2005 to 2021" Buildings 12, no. 3: 344. https://doi.org/10.3390/buildings12030344

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