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.

虚拟现实（VR）安全培训系统可以增强安全意识，同时支持各种工作条件下的健康评估。本文提出了一种用于建筑安全培训的新型VR系统，该系统通过脑电图（EEG）的脑机接口以及诸如血压和心率等生理数据来增强个人在VR中的功能。VR的使用旨在支持高水平的互动和沉浸式体验。至关重要的是，我们应用了新颖的裁剪训练算法来改善深层脑电神经网络的性能，其中包括用于实时评估的批处理归一化和ELU激活功能。它可以显着提高系统的时间效率，同时在测试数据集上保持80％以上的高精度。为了评估各种建筑环境下的工人能力，基于统计模型和工人的EEG数据开发了风险评估指标。上海市的117名建筑工人参加了这项研究。拟议的评估标准将九名参与者的脑电图确定为高度异常水平。他们接受了进一步的医学检查，其中有六人被诊断为高危健康状况。证明我们的系统在了解工人的身体状况，增强安全意识和减少事故方面发挥了重要作用。拟议的评估标准将九名参与者的脑电图确定为高度异常水平。他们接受了进一步的医学检查，其中有六人被诊断为高危健康状况。证明我们的系统在了解工人的身体状况，增强安全意识和减少事故方面发挥了重要作用。拟议的评估标准将九名参与者的脑电图确定为高度异常水平。他们接受了进一步的医学检查，其中有六人被诊断为高危健康状况。证明我们的系统在了解工人的身体状况，增强安全意识和减少事故方面发挥了重要作用。