Revealing Hidden Safety Hazards Using Workers' Collective Bodily and Behavioral Response Patterns

Supported by NSF CIS #1538029

Current hazard-identification efforts in safety management are mostly limited by humans' abilities to recognize hazards and/or by their existing knowledge of known hazards. Consequently, numerous hazards go unidentified, creating unmanageable risks. To enhance hazard recognition capabilities, this research focuses on understanding and exploiting humans' bodily and behavioral responses in their interaction with the physical environmental system. It is well recognized that a potential hazard within the system may cause instability in actions, and ultimately accidents. However, the unpredictable nature of human behavior poses a critical challenge in utilizing the analysis of such actions for the identification of unstable system conditions. Methodologies developed in this research will enable the evaluation of collective patterns associated with human responses to estimate the likelihood of hazard locations across a construction site. Thus, knowledge gained from this research will advance our ability to utilize response information for accident prevention, leading to reduced injuries and fatalities from construction-related accidents. Research outcomes will be integrated into engineering curriculum development, undergraduate research activities, industry workshops, and outreach activities for K-12 students and underrepresented student groups, especially women and minorities. 

The objective of this research is to examine whether, how, and to what extent workers' collective bodily and behavioral response patterns identify recognized/unrecognized hazards for the purpose of enhancing safety performance in construction environments. This research focuses on detecting hazards that causes fall accidents, a single most dangerous injury event within the construction industry, using workers - kinematic sensing data captured from wearable inertial measurement sensors. This research hypothesizes that the collective abnormalities apparent in multiple workers' balance and gait in one location is correlated with the likelihood of the presence (and/or the risk) of a recognized/unrecognized fall hazard in that location. To test this hypothesis, this project will: 1) identify appropriate metrics that characterize the perturbation to workers' balance and gait caused by recognized and unrecognized hazards; 2) model a near-miss index (NMI) that evaluates the abnormalities of workers' gait and balance; 3) investigate the relationship between the collective NMI patterns and the presence and risk of a hazard in each location; 4) identify and examine appropriate sensor network platforms for the scalable implementation of the approach; and 5) validate the efficacy and usefulness of the developed approach through its application within construction sites.

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Publications

Zhou, B., and M. C. Vuran, "CorTiS: Correlation-based Time Synchronization in Internet of Things", IEEE International Conference on Communications (ICC) 2019, to appear, 2019.
Guo, F., M. C. Vuran, K. Yang, and C. R. Ahn, "MPSBL: Multiple transmit power assisted sequencebased localization in wireless sensor networks", in Proc. IEEE Conference on Communications (IEEE ICC’18), Kansas City, KS, IEEE, 05/2108, 2018.  Download: 1570405897.PDF (223.77 KB)
Yang, K., C. R. Ahn, M. C. Vuran, and H. Kim, "Collective sensing of workers' gait patterns to identify fall hazards in construction", Automation in Construction, vol. Volume 82, issue October 2017, pp. 178, 10/2017.  Download: 1-s2.0-S0926580517303254-main.pdf (2.07 MB)
Zhou, B., F. Guo, and M. C. Vuran, "Demo Abstract: Clock Syntonization using CFO Information in Wireless Sensor Networks", In Computer Communications Workshops (INFOCOM WKSHPS) 2017, Atlanta, GA, May 2017.
Guo, F., B. Zhou, and M. C. Vuran, "CFOSynt: Carrier Frequency Offset Assisted Clock Syntonization for Wireless Sensor Networks", in Proc. of the 36th IEEE International Conference on Computer Communications (IEEE INFOCOM 2017), Atlanta, GA, May 2017.
Yang, K., C. R. Ahn, M. C. Vuran, and H. Kim, "Sensing Workers Gait Abnormality for Safety Hazard Identification.", 33rd International Symposium on Automation and Robotics in Construction (ISARC), Auburn, AL, 07/2016.
Yang, K., C. R. Ahn, M. C. Vuran, and S. S. Aria, "Semi-supervised Near-miss Fall Detection for Ironworkers with a Wearable Intertial Measurement Unit", Automation in Construction, vol. Volume 68, issue August 2016, pp. 202, 08/2016.  Download: 1-s2.0-S0926580516300784-main.pdf (918.67 KB)
Aria, S. S., K. Yang, C. R. Ahn, and M. C. Vuran, "Near-Miss Accident Detection for Ironworkers Using Inertial Measurement Unit Sensors", Automation and Robotics in Construction and Mining, Sydney, Australia, 07/2014.