Hazard recognition in an immersive virtual environment: Framework for the simultaneous analysis of visual search and EEG patterns
March 14, 2020 Β· Declared Dead Β· π Construction Research Congress 2020
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mojtaba Noghabaei, Kevin Han
arXiv ID
2003.09494
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CY,
eess.SP
Citations
29
Venue
Construction Research Congress 2020
Last Checked
4 months ago
Abstract
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted