Sensing workers gait abnormality for safety hazard identification

Abstract

Ironwork is considered one of the most dangerous construction trades due to its fall-prone working environment. Since safety-hazard identification is fundamental to preventing ironworkers' fall accidents, engineering measures have been applied to eliminate fall hazards or to reduce their associated risks. However, a significant quantity of hazards usually remains unidentified or not well assessed because most current efforts rely on human judgment to identify hazards. To enhance hazard identification efforts, this paper develops a technique for detecting the jobsite safety hazards of ironworkers by analyzing their gait anomalies. Using wearable inertial measurement units (WIMUs) to record kinematic data about ironworkers' gait, this study collected kinematic data while the workers interacted with two types of jobsite hazards. The anomaly level of each gait was modeled using diverse gait-related metrics. Moreover, relationships between safety hazards and worker gait abnormalities were examined through extensive experiment evaluations. The results reveal opportunities for enhancing hazard identification performance by monitoring workers' bodily response.