Virtual reality safety training using deep EEG-net and physiology data
Authors: Huang, D., Wang, X., Liu, J., Li, J. and Tang, W.
Journal: Visual Computer
Volume: 38
Issue: 4
Pages: 1195-1207
ISSN: 0178-2789
DOI: 10.1007/s00371-021-02140-3
Abstract:Virtual reality (VR) safety training systems can enhance safety awareness while supporting health assessment in various work conditions. This paper proposes a novel VR system for construction safety training, which augments an individual’s functioning in VR via a brain–computer interface of electroencephalography (EEG) and physiology data such as blood pressure and heart rate. The use of VR aims to support high levels of interactions and immersion. Crucially, we apply novel clipping training algorithms to improve the performance of a deep EEG neural network, including batch normalization and ELU activation functions for real-time assessment. It significantly improves the system performance in time efficiency while maintaining high accuracy of over 80% on the testing datasets. For assessing workers’ competence under various construction environments, the risk assessment metrics are developed based on a statistical model and workers’ EEG data. One hundred and seventeen construction workers in Shanghai took part in the study. Nine of the participants’ EEG is identified with highly abnormal levels by the proposed evaluation metric. They have undergone further medical examinations, and among them, six are diagnosed with high-risk health conditions. It proves that our system plays a significant role in understanding workers’ physical condition, enhancing safety awareness, and reducing accidents.
https://eprints.bournemouth.ac.uk/35446/
Source: Scopus
Virtual Reality Safety Training Using Deep EEG-net and Physiology Data
Authors: Huang, D., Wang, X., Liu, J., Li, J. and Tang, W.
Journal: The Visual Computer
Publisher: Springer Nature
ISSN: 0178-2789
Abstract:Virtual reality (VR) safety training systems can enhance safety awareness whilst supporting health assessment in various work conditions. This paper proposes a novel VR system for construction safety training, which augments an individual’s functioning in VR via a Brain-Computer Interface (BCI) of electroencephalography (EEG) and physiology data such as blood pressure and heart rate. The use of VR aims to support high levels of interactions and immersion. Crucially, we apply novel clipping training algorithms to improve the performance of a deep EEG neural network, including batch normalization and ELU activation functions for real-time assessment.
It significantly improves the system performance in time efficiency whilst maintaining high accuracy of over 80% on the testing datasets. For assessing workers’ competence under various construction environments, the risk assessment metrics are developed based on a statistical model and workers’ EEG data. 117 construction workers in Shanghai took part in the study. Nine of the participants’ EEG is identified with highly abnormal levels by the proposed evaluation metric. They have undergone further medical examinations, and among them, six are diagnosed with high-risk health conditions. It proves that our system plays a significant role in understanding workers’ physical condition, enhancing safety awareness, and reducing accidents
https://eprints.bournemouth.ac.uk/35446/
Source: Manual
Virtual Reality Safety Training Using Deep EEG-net and Physiology Data.
Authors: Huang, D., Wang, X., Liu, J., Li, J. and Tang, W.
Journal: Visual Computer
Volume: 38
Pages: 1195-1207
ISSN: 0178-2789
Abstract:Virtual reality (VR) safety training systems can enhance safety awareness whilst supporting health assessment in various work conditions. This paper proposes a novel VR system for construction safety training, which augments an individual’s functioning in VR via a Brain-Computer Interface (BCI) of electroencephalography (EEG) and physiology data such as blood pressure and heart rate. The use of VR aims to support high levels of interactions and immersion. Crucially, we apply novel clipping training algorithms to improve the performance of a deep EEG neural network, including batch normalization and ELU activation functions for real-time assessment. It significantly improves the system performance in time efficiency whilst maintaining high accuracy of over 80% on the testing datasets. For assessing workers’ competence under various construction environments, the risk assessment metrics are developed based on a statistical model and workers’ EEG data. 117 construction workers in Shanghai took part in the study. Nine of the participants’ EEG is identified with highly abnormal levels by the proposed evaluation metric. They have undergone further medical examinations, and among them, six are diagnosed with high-risk health conditions. It proves that our system plays a significant role in understanding workers’ physical condition, enhancing safety awareness, and reducing accidents
https://eprints.bournemouth.ac.uk/35446/
Source: BURO EPrints