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.