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.
Weighing votes in human-machine collaboration for hazard recognition: Inferring hazard perceptual threshold and decision confidence from electroencephalogram wavelets
Deep Learning-Based Visual Fatigue Detection Using Eye Gaze Patterns in VR
Dept. of Computer Science Clarkson University Potsdam, New York, USA（计算机科学系 克拉克逊大学）；David D. Reh School of Business Clarkson University Potsdam, New York, USA（戴维·D·雷商学院 克拉克逊大学）
