Image-based construction hazard avoidance system using augmented reality in wearable device

Kim, Kinam; Kim, Hongjo; Kim, Hyoungkwan

doi:10.1016/j.autcon.2017.06.014

Cited by 154 publications

(97 citation statements)

References 25 publications

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“…Other researchers have utilized vision-based approaches (e.g., depth cameras) to analyze workers' motions to identify safety hazards (Ray and Teizer, 2012;Han and Lee, 2013;Kim et al, 2017;Yuan et al, 2017). Kim et al (2017) proposed a vision-based hazard avoidance system that proactively informs workers of potentially dangerous situations. It was reported that the proposed system can mitigate hazards and improve construction site safety.…”

Section: Introductionmentioning

confidence: 99%

Validity and reliability of a wearable insole pressure system for measuring gait parameters to identify safety hazards in construction

Antwi‐Afari

Seo

et al. 2020

ECAM

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PurposeConstruction workers are frequently exposed to safety hazards on sites. Wearable sensing systems (e.g. wearable inertial measurement units (WIMUs), wearable insole pressure system (WIPS)) have been used to collect workers' gait patterns for distinguishing safety hazards. However, the performance of measuring WIPS-based gait parameters for identifying safety hazards as compared to a reference system (i.e. WIMUs) has not been studied. Therefore, this study examined the validity and reliability of measuring WIPS-based gait parameters as compared to WIMU-based gait parameters for distinguishing safety hazards in construction.Design/methodology/approachFive fall-risk events were conducted in a laboratory setting, and the performance of the proposed approach was assessed by calculating the mean difference (MD), mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and intraclass correlation coefficient (ICC) of five gait parameters.FindingsComparable results of MD, MAE, MAPE and RMSE were found between WIPS-based gait parameters and the reference system. Furthermore, all measured gait parameters had validity (ICC = 0.751) and test-retest reliability (ICC = 0.910) closer to 1, indicating a good performance of measuring WIPS-based gait parameters for distinguishing safety hazards.Research limitations/implicationsOverall, this study supports the relevance of developing a WIPS as a noninvasive wearable sensing system for identifying safety hazards on construction sites, thus highlighting the usefulness of its applications for construction safety research.Originality/valueThis is the first study to examine the performance of a wearable insole pressure system for identifying safety hazards in construction.

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Section: Introductionmentioning

confidence: 99%

Validity and reliability of a wearable insole pressure system for measuring gait parameters to identify safety hazards in construction

Antwi‐Afari

Seo

et al. 2020

ECAM

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“…Zollmann et al [23] employed AR to visualize 3D building models reconstructed from aerial images for construction progress monitoring. In [24], computer vision techniques were applied to video feed from both global and personal perspective to detect potential hazards on construction sites. The identified hazardous information is then overlain on worker's view through AR.…”

Section: Urban Outdoor Armentioning

confidence: 99%

Evaluation of Augmented Reality-Based Building Diagnostics Using Third Person Perspective

Liu

Jonsson

Seipel

2020

IJGI

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Comprehensive user evaluations of outdoor augmented reality (AR) applications in the architecture, engineering, construction and facilities management (AEC/FM) industry are rarely reported in the literature. This paper presents an AR prototype system for infrared thermographic façade inspection and its evaluation. The system employs markerless tracking based on image registration using natural features and a third person perspective (TPP) augmented view displayed on a hand-held smart device. We focus on evaluating the system in user experiments with the task of designating positions of heat spots on an actual façade as if acquired through thermographic inspection. User and system performance were both assessed with respect to target designation errors. The main findings of this study show that positioning accuracy using this system is adequate for objects of the size of one decimeter. After ruling out the system inherent errors, which mainly stem from our application-specific image registration procedure, we find that errors due to a human’s limited visual-motoric and cognitive performance, which have a more general implication for using TPP AR for target designation, are only a few centimeters.

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“…In addition, the combination of big data and AI will produce strong flexibility, adaptability and learning ability, which is of great significance for innovation of traditional safety management methods. Wang Wen et al (Wang et al, 2018) developed the tunnel safety monitoring system by deeply learning the video big data of the tunnel installation site through convolution neural network, which realized the intelligent management of risk early warning and tunnel abnormal event monitoring; Kinam Kim (Kim et al, 2017) developed the machine vision hazard avoidance system through the machine learning of the construction big data of the site to remind workers of potential safety risks. The above literature provides a case study and reference for the feasibility of the application of big data combined with AI in engineering safety management, and affirms the prac-ticability and efficiency of video monitoring in personnel safety management and risk early warning intelligent management.…”

Section: Summary Of Personnel Safety Management Under Big Datamentioning

confidence: 99%

Research on Safety Management of Construction Engineering Personnel under “Big Data + Artificial Intelligence”

Yi¹,

Wu²

2020

OJBM

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Under the background of the information age, traditional engineering safety management is undergoing a profound change. The introduction of big data and artificial intelligence technologies in construction project safety management is becoming increasingly widespread. Based on literature analysis, combined with artificial intelligence and big data in construction safety management in the application is still in the primary stage, especially in applied research personnel safety management rarely literature. A review of the exploration and application of construction engineering safety management under the combination of artificial intelligence and big data, the analysis and summary of the current status and trends of the two technologies in construction engineering safety management, and the design of Based on artificial intelligence engineering personnel safety management of large data systems architecture, enrich and improve construction safety of personnel management concepts and technical means.

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Image-based construction hazard avoidance system using augmented reality in wearable device

Cited by 154 publications

References 25 publications

Validity and reliability of a wearable insole pressure system for measuring gait parameters to identify safety hazards in construction

Validity and reliability of a wearable insole pressure system for measuring gait parameters to identify safety hazards in construction

Evaluation of Augmented Reality-Based Building Diagnostics Using Third Person Perspective

Research on Safety Management of Construction Engineering Personnel under “Big Data + Artificial Intelligence”

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