Virtual Reality for Training and Fitness Assessments for Construction Safety
Authors: Huang, D., Wang, X., Li, J. and Tang, W.
Journal: Proceedings - 2020 International Conference on Cyberworlds, CW 2020
Pages: 172-179
ISBN: 9781728164977
DOI: 10.1109/CW49994.2020.00036
Abstract:Reducing accident rate is a primary goal of construction safety. In this paper, we present a large scale study of using virtual reality technology for safety training. Beyond the training, a technology framework is proposed to assess the fitness of construction workers (e.g. suitability of people with underlining health conditions to work under particular construction environments). The new virtual construction system consists of a Brain-Computer Interface (BCI) of electroencephalography (EEG) neural network to capture EEG signals of users during the virtual simulation training continuously to achieve user profiling. For real-time assessment of the accident susceptibility of a worker under various construction environments, a deep learning neural network is trained to process the EEG crops and a clipping training algorithm that classifies small segments of the EEG dataset is used to improve the computational performance of the system. Physiology data of the person during the training, i.e. blood pressure and heart rate, is also recorded. Based on the EEG data and the physiology data, a statistic model is used in the safety assessment framework to set up the risk standard. The study has tested 117 workers who were employed by the construction sites in Shanghai. People who were tested in the risk group were further underwent medical examinations for risk related medical conditions that deemed unsuitable for working in construction sites. Results show six of the nine workers identified by the VR system have been medically confirmed unsuitable, thus, over 80% accuracy of our virtual reality training and assessment system. Our proposed system can be used as a tool for understanding risk conditions of workers and safety training.
https://eprints.bournemouth.ac.uk/36732/
Source: Scopus
Virtual Reality for Training and Fitness Assessments for Construction Safety
Authors: Huang, D., Wang, X., Li, J. and Tang, W.
Conference: 2020 International Conference on Cyberworlds (CW)
Dates: 29 September-1 October 2020
DOI: 10.1109/CW49994.2020.00036
Abstract:Reducing accident rate is a primary goal of construction safety. In this paper, we present a large scale study of using virtual reality technology for safety training. Beyond the training, a technology framework is proposed to assess the fitness of construction workers (e.g. suitability of people with underlining health conditions to work under particular construction environments). The new virtual construction system consists of a Brain-Computer Interface (BCI) of electroencephalography (EEG) neural network to capture EEG signals of users during the virtual simulation training continuously to achieve user profiling. For real-time assessment of the accident susceptibility of a worker under various construction environments, a deep learning neural network is trained to process the EEG crops and a clipping training algorithm that classifies small segments of the EEG dataset is used to improve the computational performance of the system. Physiology data of the person during the training, i.e. blood pressure and heart rate, is also recorded. Based on the EEG data and the physiology data, a statistic model is used in the safety assessment framework to set up the risk standard. The study has tested 117 workers who were employed by the construction sites in Shanghai. People who were tested in the risk group were further underwent medical examinations for risk related medical conditions that deemed unsuitable for working in construction sites. Results show six of the nine workers identified by the VR system have been medically confirmed unsuitable, thus, over 80% accuracy of our virtual reality training and assessment system. Our proposed system can be used as a tool for understanding risk conditions of workers and safety training
https://eprints.bournemouth.ac.uk/36732/
https://ieeexplore.ieee.org/abstract/document/9240509/authors#authors
Source: Manual
Virtual reality for training and fitness assessments for construction safety
Authors: Huang, D., Wang, X., Liu, J., Li, J. and Tang, W.
Conference: 2020 International Conference on Cyberworlds (CW)
Abstract:Reducing accident rate is a primary goal of construction safety. In this paper, we present a large scale study of using virtual reality technology for safety training. Beyond the training, a technology framework is proposed to assess the fitness of construction workers (e.g. suitability of people with underlining health conditions to work under particular construction environments). The new virtual construction system consists of a Brain-Computer Interface (BCI) of electroencephalography (EEG) neural network to capture EEG signals of users during the virtual simulation training continuously to achieve user profiling. For real-time assessment of the accident susceptibility of a worker under various construction environments, a deep learning neural network is trained to process the EEG crops and a clipping training algorithm that classifies small segments of the EEG dataset is used to improve the computational performance of the system. Physiology data of the person during the training, i.e. blood pressure and heart rate, is also recorded. Based on the EEG data and the physiology data, a statistic model is used in the safety assessment framework to set up the risk standard. The study has tested 117 workers who were employed by the construction sites in Shanghai. People who were tested in the risk group were further underwent medical examinations for risk related medical conditions that deemed unsuitable for working in construction sites. Results show six of the nine workers identified by the VR system have been medically confirmed unsuitable, thus, over 80% accuracy of our virtual reality training and assessment system. Our proposed system can be used as a tool for understanding risk conditions of workers and safety training
https://eprints.bournemouth.ac.uk/36732/
https://cyberworlds2020.sciencesconf.org/
Source: BURO EPrints