Earthquake Safety Training through Virtual Drills

Changyang Li1,2    Wei Liang1    Chris Quigley2    Yibiao Zhao3    Lap-Fai Yu2

1Beijing Institute of Technology    2University of Massachusetts Boston    3Massachusetts Institute of Technology   



Recent popularity of consumer-grade virtual reality devices, such as the Oculus Rift and the HTC Vive, has enabled household users to experience highly immersive virtual environments. We take advantage of the commercial availability of these devices to provide an immersive and novel virtual reality training approach, designed to teach individuals how to survive earthquakes, in common indoor environments. Our approach makes use of virtual environments realistically populated with furniture objects for training. During a training, a virtual earthquake is simulated. The user navigates in, and manipulates with, the virtual environments to avoid getting hurt, while learning the observation and self-protection skills to survive an earthquake. We demonstrated our approach for common scene types such as offices, living rooms and dining rooms. To test the effectiveness of our approach, we conducted an evaluation by asking users to train in several rooms of a given scene type and then test in a new room of the same type. Evaluation results show that our virtual reality training approach is effective, with the participants who are trained by our approach performing better, on average, than those trained by alternative approaches in terms of the capabilities to avoid physical damage and to detect potentially dangerous objects.


Earthquake Safety Training through Virtual Drills
Changyang Li, Wei Liang, Chris Quigley, Yibiao Zhao, Lap-Fai Yu
IEEE Transactions on Visualization and Computer Graphics (Special Issue on IEEE Virtual Reality 2017)
Paper , Video


    title= {Earthquake Safety Training through Virtual Drills},
    author = {Li, Changyang and Liang, Wei and Quigley, Chris and Zhao, Yibiao and Yu, Lap-Fai},
    journal = {IEEE Transactions on Visualization and Computer Graphics},
    volume = {23(4)},
    pages = {1275 - 1284},
    year = {2017},
    publisher = {IEEE}


  • 媒体计算与智能系统实验室

  • Media Computing and Intelligent Systems Lab

Beijing Institute of Technology Copyright Address: 5 South Zhongguancun

Street, Haidian District, Beijing Postcode: 100081