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Researching how stress spreads during evacuations

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Category: research Video duration: Researching how stress spreads during evacuations
Evacuating a space quickly and safely during an emergency has life-saving consequences, but it is known that feeling stressed impacts a person's ability to exit a building quickly. Nicole Abaid, associate professor in the department of mathematics, is working alongside graduate students from Northern Illinois University and the University of Virginia to develop a model that could help understand how to make evacuations safer.
We're doing an experiment in The Cube at the Moss Arts Center on human crowd behavior. We're trying to understand how people move together in groups under stress. That would be like any sort of evacuation, a fire drill, or a real fire, or anything like that. (Siren) And so the issue is, when you have a real situation like that, you can't ethically test under those conditions. So it's really important to have actual data from people and then to build a robust model that can predict what would happen in situations we can't test. "Here. Here. Here." In reality, there's a lot of ways that certain situations such as this can go wrong. What's a way that we can go about this safely? And this experiment is actually, like, calculating that or, you know, trying to. This work is being done primarily by this team of students who have been amazing. So we have graduate students who are visiting from Northern Illinois University and from UVA, and then undergraduates, both employees and volunteers who have been incredible. And then working with the staff at ICAT, they made the space available to us, and also really helped in building the setup that we're using. They're like, magicians. The Cube is a black box theater. I call it a theater, but it is a research facility, too. It is made for, like, reconfiguring. So if you need to recreate an immersive environment, the cube is like the go to place for that. There's a lot of different data that has to be synchronized, so it's an extremely challenging set of data to collect. We're taking physiologic measurements of all the participants who navigate the maze that we built. We're using the because it has this amazing motion capture capability. So we know where they are. We know where they're looking to see what types of cues they're bringing in. The model that we are going to build with it is going to be revolutionary because the data in itself is so complex, but even more than the model that we build, we plan to make the data set public, so other people can use it to do other things.