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Automating Analysis of Construction Workers Viewing Patterns for Personalized Safety Training and Management
Unrecognized hazards increase the likelihood of workplace fatalities and...
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Virtual Manipulation in an Immersive Virtual Environment: Simulation of Virtual Assembly
To fill the lack of research efforts in virtual assembly of modules and ...
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Real-world Mapping of Gaze Fixations Using Instance Segmentation for Road Construction Safety Applications
Research studies have shown that a large proportion of hazards remain un...
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The Use of Machine Learning and Big Five Personality Taxonomy to Predict Construction Workers' Safety Behaviour
Research has found that many occupational accidents are foreseeable, bei...
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Non-linearity identification for construction workers' personality-safety behaviour predictive relationship using neural network and linear regression modelling
The prediction of workers' safety behaviour can help identify vulnerable...
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Experiencing Extreme Height for The First Time: The Influence of Height, Self-Judgment of Fear and a Moving Structural Beam on the Heart Rate and Postural Sway During the Quiet
Falling from elevated surfaces is the main cause of death and injury at ...
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Riposte! A Large Corpus of Counter-Arguments
Constructive feedback is an effective method for improving critical thin...
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Hazard recognition in an immersive virtual environment: Framework for the simultaneous analysis of visual search and EEG patterns
Unmanaged hazards in dangerous construction environments proved to be one of the main sources of injuries and accidents. Hazard recognition is crucial to achieve effective safety management and reduce injuries and fatalities in hazardous job sites. Still, there has been lack of effort that can efficiently assist workers in improving their hazard recognition skills. This study presents virtual safety training in an Immersive Virtual Environment (IVE) to enhance worker's hazard recognition skills. A worker wearing a Virtual Reality (VR) device, that is equipped with an eye-tracker, virtually recognizes hazards on simulated construction sites while a brainwave-sensing device records brain activities. This platform can analyze the overall performance of the workers in a visual hazard recognition task and identify hazards that need additional intervention for each worker. This study provides novel insights on how a worker's brain and eye act simultaneously during a visual hazard recognition process. The presented method can take current safety training programs into another level by providing personalized feedback to the workers.
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