Automating Analysis of Construction Workers Viewing Patterns for Personalized Safety Training and Management
This research addresses safety issues for construction workers by developing a tool to improve hazard recognition, though it appears incremental as it builds on existing correlations between viewing patterns and performance.
The study tackled the problem of unrecognized hazards in construction by analyzing workers' viewing patterns as a visual search process, proposing a framework for a vision-based tool to record and analyze these patterns for personalized safety training and management.
Unrecognized hazards increase the likelihood of workplace fatalities and injuries substantially. However, recent research has demonstrated that a large proportion of hazards remain unrecognized in dynamic construction environments. Recent studies have suggested a strong correlation between viewing patterns of workers and their hazard recognition performance. Hence, it is important to study and analyze the viewing patterns of workers to gain a better understanding of their hazard recognition performance. The objective of this exploratory research is to explore hazard recognition as a visual search process to identifying various visual search factors that affect the process of hazard recognition. Further, the study also proposes a framework to develop a vision based tool capable of recording and analyzing viewing patterns of construction workers and generate feedback for personalized training and proactive safety management.