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Tracking Divergence in Workers’ Trajectory Patterns for Hazard Sensing in Construction Kanghyeok Yang1; Changbum R. Ahn2; and Hyunsoo Kim3 1
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Ph.D. Candidate, Construction Engineering and Management, The Charles Durham School of Architectural Engineering and Construction, Univ. of Nebraska-Lincoln, W113 Nebraska Hall, Lincoln, NE 68588. E-mail:
[email protected] 2 Associate Professor, Dept. of Construction Science, Texas A&M Univ., 330B Francis Hall, College Station, TX 77840. E-mail:
[email protected] 3 Assistant Professor, Dept. of Architectural Engineering, Gyeongnam National Univ. of Science and Technology, 33, Dongjin-ro, Jinju-si, Gyeongsangnam-do 52725, Republic of Korea. E-mail:
[email protected] Abstract In construction, hazard identification is a critical first step in safety management. To complement current hazard identification practices that rely on manual inspection, our previous study proposed an approach to identifying hazards by analyzing workers’ collective behavioral responses—particularly the collective abnormality of workers’ gait patterns as observed via foot-worn inertial measurement units. Alternatively, workers’ moving trajectories represent behavioral responses that may contain important information about site conditions, since workers tend to change their walking path to avoid the risks posed by hazards. In this context, this paper presents an approach to quantitatively assess the level of divergence within workers’ collective moving trajectories. The proposed approach first defines a representative trajectory from the quadratic polynomial regression–fitting of a given dataset and then assesses how divergent a moving trajectory is by quantifying the dissimilarity between each worker’s moving trajectory and its representative trajectory. The results from a laboratory experiment simulating bricklaying activities demonstrates that such measurements of divergent trajectories can indicate the location of hazards. INTRODUCTION Given the labor-intensive nature of construction work, preventing occupational injuries and fatalities is paramount to both maintain the productivity levels of a construction project and to reduce the costs resulting from injuries. Hazard identification is the first step in preventing such occupational injuries and fatalities. However, current practices rely heavily upon safety managers’ and workers’ visual inspection (Albert et al. 2014b), which can be critically hindered due to the dynamic nature of a construction site and the limited amount of resources available for inspection. Consequently, many hazards remain unidentified, which increases the risk of injuries on a construction site (Carter and Smith 2006). This situation raises the need for an approach to complement current hazard identification practices. In this context, our previous studies (Yang et al. 2017a; Yang et al. 2017b) found that the analysis of the collective behavioral responses of workers can provide
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valuable information when trying to identify latent hazards on a jobsite. Specifically, our previous studies quantified the collective abnormality of workers’ gait patterns and indicated that the abnormality patterns relate to the location of some hazards. However, these studies reveal a need for incorporating other types of behavioral responses, as some types of hazards do not create any change in gait pattern. In particular, the analysis of workers’ moving trajectories could provide key information on site conditions (Kim et al. 2016), because such trajectories may reflect workers’ decisions to avoid the safety risks posed by hazardous site conditions. Since hazardous site conditions may diversify workers’ responses when selecting their moving trajectories (e.g., detour, do not detour), the level of divergence in the collective moving trajectories of workers in one location may provide important information regarding hazards. To this end, this study presents an approach that can measure the divergence of workers’ collective moving trajectories and demonstrates how such measurements can be useful in inferring the site conditions. RESEARCH BACKGROUND Due to the importance of hazard identification, many previous studies have applied diverse approaches (e.g., safety training) to increase the knowledge of construction workers or safety managers. Albert et al. (2014a) introduced a model that can increase hazard identification performance and tested its usefulness in an actual construction project. Sacks et al. (2013) proposed conducting safety training in a virtual reality environment and tested the feasibility and success of the approach in increasing hazard identification performance. However, these approaches still depend on human inspections and therefore face several limitations, including limited resources and the dynamic nature of construction sites. Furthermore, the effectiveness of human inspection can be easily influenced by environmental factors such as light conditions and visual interference by other tasks, which makes this approach to identifying hazards suboptimal. With the development of data processing techniques and wearable sensors, several studies have attempted to identify different types of wearable sensors that can be used to prevent occupational injuries or to identify hazards via the analysis of a worker’s bodily movements. Jebelli utilized a WIMU and tested the feasibility of measuring worker’s gait stability (Jebelli et al. 2016a) and postural stability (Jebelli et al. 2016b) as a means of preventing fall accidents. Cheng et al. (2013) introduced a physiological monitoring system for the ergonomic analysis of a worker to prevent musculoskeletal injuries. Additionally, some researchers utilized a worker’s locational data to identify hazard areas through a real-time location tracking system. Kim et al. (2016) developed automated hazard identification methods by measuring the differences between optimal route and actual location data. Li et al. (2017) introduced an automated hazard-zone tracking approach that used crowd-sourced location data blended with historical location data. These studies revealed the opportunity and advantage of analyzing data from wearable sensor for enhancing construction safety. Our previous studies (Kim et al. 2017; Yang et al. 2016, 2017) attempted to infer hazard locations by analyzing the changes in gait/body movement patterns as
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characterized using data from wearable inertial measurement sensors attached to subjects’ bodies and feet. These studies indicated that collective anomalies in gait patterns/body movements strongly correlate with the location of hazards installed in the laboratory experiments. However, such responses captured from WIMUs only reflected situation in which subjects directly interacted with hazards (e.g., stepped on the hazard). If subjects chose to detour around hazards to avoid the risk, the data did not manifest any noticeable patterns in the WIMU dataset. In this context, this study focuses on analyzing the collective moving trajectory of workers and explores how divergence within the collective moving trajectories of workers can be quantified and related to hazardous locations. METHODOLOGY In order to measure the level of divergence within workers’ collective moving trajectories, this study assesses the degree of dissimilarity among individual trajectories within the area of interest. As its first step, a reference trajectory that represented the overall trajectory within the area is defined. All of the trajectory data were smoothed using a one-second moving average window filter, and then a quadratic polynomial regression–fitting approach was applied to derive a reference trajectory from all the trajectory data within an area of interest. Given the calculated reference trajectory, the analysis quantified the dissimilarity between each individual trajectory and the reference trajectory using the discrete Fréchet distance. The discrete Fréchet distance measures the geometric similarity of two different curves by determining the shortest leash that can sufficiently traverse two paths on the curves. It has been used to measure pedestrian and vehicle trajectories (Bang et al. 2016). The mathematical expression of the Fréchet distance appears in Equation 1. ℎ Where, and d is the distance between
= min max ,
,
−
(1)
are locations of curve A and curve B, respectively, and and .
The amount of divergence occurring within the collective moving trajectories is then computed by summing the Fréchet distance of each individual trajectory as compared to the reference trajectory. Our approach to use the reference trajectory allows maintaining a low computational cost, compared to quantifying the dissimilarity between all possible pairs of individual trajectories. In order to compare between different areas of interest, the cumulative Fréchet distance of an area is normalized using the number of individual trajectories included in the area. The following sections illustrate how the proposed approach can be used to quantify the collective moving trajectory of subjects in a laboratory setting. EXPERIMENTAL SETTING AND DATA COLLECTION Laboratory experiments were designed and conducted to examine how collective moving trajectories can be affected by the existence of hazards (See Figure
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1 for details). The first experiment (Figure 1-left) was conducted under a no-hazard condition, and the second experiment (Figure 1-right) was conducted with several installed hazards. A total of five subjects participated in each experiment, and no subjects participated in both experiments. Subjects were asked to perform a masonry task that included picking up materials, moving materials, and installing bricks. Specifically, a subject started at the installation location, visited the material stockpile, picked up the bricks, and then revisited the installation area; each subject repeated the described sequence 27 times. All subjects were equipped with safety gear (gloves, boots, and a vest) during the experiment. Additionally, each subject wore an ultrawideband (Ubisense Inc.) tag for real time location tracking on the subject’s safety helmet, and the ultra-wideband tag collected X, Y, and Z coordinates for the tag with a 10-Hz frequency. Also, all experiment procedures were video recorded for further validation, and all required processing of this study was performed in MATLAB (R2016B, Mathworks). Install
Install
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Figure 1. Experiment layouts without hazards (left) and with hazards (right) Subjects were only allowed to walk clockwise through the experiment to confirm that they were exposed to each hazard the same number of times. The total size of the experiment area was 18 m (width) and 9.5 m height while limited to have only 2.5m width walk-path using barricades to prevent an access to the inner area. For hazardous areas of interest, this study installed six different types of hazards that are frequently observed in indoor construction environments. The detail of each hazard and installed location appears in Table 1. Table 1. Installed Safety Hazards for Experiments Types of Hazards H1 H2 H3 H4 H5 H6
Falling Object Area Unorganized Cords Avoidable Hazards Slippery Surface Unavoidable Obstacles Unorganized Pipes
Size (m x m) 1.3 x 1.0 2.3 x 2.5 1.3 x 1.3 0.8 x 2.5 1.0 x 2.5 2.5 x 0.8
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Location (in Figure 2) A2 A3 A5 A8 A10 A11
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VISUALIZATION OF MOVING TRAJECTORIES
Y coordinate(m)
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Figure 2 visualizes the overall recorded trajectory data of the subjects (left: without hazards, right: with hazards). A total of 135 moving trajectory lines were included in each figure, and the total experiment setting was divided up into reference areas for comparison. The comparison of these figures indicates that some installed hazards clearly created divergent moving trajectories. For example, A3, A5, A8 and A10 include installed hazards, and more than one moving trajectory stream appears in those areas.
A11
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Figure 2. Workers’ trajectory patterns in experiments without hazards (left) and with hazard (right). Figure 3 provides a closer look at the trajectory patterns in the hazardous locations. Clear differences were observed between the no-hazard and hazard cases. In the cases of Hazard 2, 3, 4, and 5, multiple distinct streams of trajectories appear, whereas in the no-hazard case, only one main stream of trajectories appear. This difference indicates that some subjects took a different route to avoid the hazards. Hazard 6 does not present multiple streams of trajectories, but the individual trajectories were more diversified as compared to the no-hazard counterfactual. On the contrary, Hazard 1 presents more streamlined trajectories compared to its nohazard case. This situation is because Hazard 1 (the falling object area) represents a complete blockage of subjects’ moving paths, whereas other hazards still allow subjects to pass through them. These results confirm that analyzing the collective moving trajectories of workers can provide valuable information about site conditions; further, these results indicate the benefits of quantitatively assessing the divergence of collective moving trajectories as a tool for targeting hazard identification in a dynamic area.
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Fig gure 3. Work ker’s trajecto ory patterns both with annd without thhe existencee of hazards ME EASURING G DIVERGE ENT TRAJE ECTORIES S Thee divergencee within the collective moving m trajecctories was ccomputed byy measuring the dissimilarity between in ndividual traajectories annd their referrence trajecttory in each nts the referrence trajectoory formulaated in each area. The redliine in Figuree 3 represen ure 4 illustraates the level of trajecttory divergeence manifeested across area, and Figu bjects in each h area. In acccordance with the visuaalized resultss, Hazard 2, 3, 4, and 6 sub (corresponding g to Area 3,, 5, 8, and 11) manifesst a higher level of divvergence as com mpared to th heir no-hazarrd counterpaarts, and the ddivergent traajectories inn these areas are 12% to 60% % higher th han those of their no-hazzard cases. In particularr, Hazard 6 (Arrea 3) also presented p a higher diveergence valuue, although it was not observed a majjor diversion n of trajectorry streams in n Hazard 6. When looking at th he trends in the divergennce values w within the W With-Hazard settting, the peaak divergencces manifest in the areass with the haazards, nameely, areas 3, 5, 6, 6 and 11. This T result in ndicates that quantifyingg the trajectoory of diverggence could pro ovide importtant informaation withou ut the need for control group dataa. However, Areea 10—with Hazard 5— —did not pressent any distiinct patternss in terms off divergence from m nearby area values nor n from itss no-hazard control; hoowever, visuualizing the trajectories in this t area (Fiigure 3-Areaa 10) revealled multiple streams of trajectories around the hazzard. This reesult may be partially ddue to the ffact that Areea 10’s nohazzard case alsso manifesteed slightly diversified d ttrajectories; if so, this explanation
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reveals that the presented approach does not address divergent trajectory streams as mere measures of the dissimilarity between individual trajectories and their reference trajectory. Future research will be necessary to recognize the diversion of trajectory streams and represent these diversions in a quantifiable way.
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H1
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Figure 4. Trajectory Divergence Measurement Results CONCLUSION This study presents an approach to quantifying the level of divergence in the collective trajectories of workers’ moving paths. The results from this case study indicate that hazards in the laboratory setting yielded divergent trajectories within subjects’ collective moving trajectories, and the proposed approach was able to quantify such observed divergences. These findings show that workers’ moving trajectories manifest behavioral responses to their environments. When combined with the analysis of other behavioral responses, the analysis of these collective moving trajectories may enable researchers and users to better discern latent or unidentified hazards from the collective behavior response patterns of workers on construction jobsites. Such a success would open the door for improved hazard identification and increased safety for construction workers. ACKNOWLEGEMENTS The authors would like to acknowledge Dr. Terry Stentz—Associate Professor, Durham School of Architectural Engineering and Construction, UNL—for designing and conducting the experiment. This study was financially supported by the NSF grant CMMI #1538029. Any opinions, findings, conclusions, or recommendations expressed in this article are those of the authors and do not necessarily reflect the views of the National Science Foundation. REFERENCES Albert, A., Hallowell, M., and Kleiner, B. (2014a). “Enhancing Construction Hazard Recognition and Communication with Energy-Based Cognitive Mnemonics and Safety Meeting Maturity Model: Multiple Baseline Study.” Journal of Construction Engineering and Management, 140(2), 04013042. © ASCE Construction Research Congress 2018
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Albert, A., Hallowell, M. R., Kleiner, B., Chen, A., and Golparvar-Fard, M. (2014b). “Enhancing Construction Hazard Recognition with High-Fidelity Augmented Virtuality.” Journal of Construction Engineering and Management, 140(7), 04014024. Bang, Y., Kim, J., and Yu, K. (2016). “An Improved Map-Matching Technique Based on the Fréchet Distance Approach for Pedestrian Navigation Services.” Sensors (Basel, Switzerland), 16(10). Carter, G., and Smith, S. (2006). “Safety Hazard Identification on Construction Projects.” Journal of Construction Engineering and Management, 132(2), 197–205. Cheng, T., Migliaccio, G., Teizer, J., and Gatti, U. (2013). “Data Fusion of Real-Time Location Sensing and Physiological Status Monitoring for Ergonomics Analysis of Construction Workers.” Journal of Computing in Civil Engineering, 27(3), 320–335. Jebelli, H., Ahn, C. R., and Stentz, T. L. (2016a). “Comprehensive Fall-Risk Assessment of Construction Workers Using Inertial Measurement Units: Validation of the Gait-Stability Metric to Assess the Fall Risk of Iron Workers.” Journal of Computing in Civil Engineering, 30(3), 04015034. Jebelli, H., Ahn, C. R., and Stentz, T. L. (2016b). “Fall risk analysis of construction workers using inertial measurement units: Validating the usefulness of the postural stability metrics in construction.” Safety Science, 84, 161–170. Kim, H., Lee, H.-S., Park, M., Chung, B., and Hwang, S. (2016). “Automated hazardous area identification using laborers’ actual and optimal routes.” Automation in Construction, 65, 21–32. Kim Hyunsoo, Ahn Changbum R., and Yang Kanghyeok. (2017). “Identifying Safety Hazards Using Collective Bodily Responses of Workers.” Journal of Construction Engineering and Management, 143(2), 04016090. Li, H., Yang, X., Skitmore, M., Wang, F., and Forsythe, P. (2017). “Automated classification of construction site hazard zones by crowd-sourced integrated density maps.” Automation in Construction, 81, 328–339. Sacks, R., Perlman, A., and Barak, R. (2013). “Construction safety training using immersive virtual reality.” Construction Management and Economics, 31(9), 1005–1017. Yang, K., Ahn, C. R., Vuran, M. C., and Kim, H. (2016). “Sensing Workers Gait Abnormality for Safety Hazard Identification.” Yang, K., Ahn, C. R., Vuran, M. C., and Kim, H. (2017a). “Collective sensing of workers’ gait patterns to identify fall hazards in construction.” Automation in Construction, 82, 166–178. Yang, K., Ahn, C., Vuran, M. C., and Kim, H. (2017b). “Analyzing Spatial Patterns of Workers’ Gait Cycles for Locating Latent Fall Hazards.” Computing in Civil Engineering 2017, 458–466.
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