Concussions are a major issue, not just for athletes, but for a wide range of professions. These, along with other types of brain injuries, are usually identified by the eyes. Normally, pupils are able to dilate when the light around them changes, starting small in bright areas and growing bigger in dark ones, changing size often in under a second. With a concussion, however, this reaction time for the change in the pupil is delayed, which is one of the major ways of identifying such an injury. The sooner a traumatic brain injury is recognized, the better it will be for the patient, and the lower the chance of permanent brain damage in severe cases.
Meet Team Headspace, made up of Stevens Class of 2021 seniors Zamin Akmal, Nicole Chresomales, Amanda Delorme, and Sophie Makepeace. Their job was to look at this issue of identifying concussions and come up with a potential solution.
Their work was a part of the Capstone Marketplace Project, which develops technology for the defense industry under the Systems Engineering Research Center led by Stevens. As the project was sponsored by United States Army Special Operations Command, team Headspace had the extra challenge of developing a solution that could be easily used in a combat environment.
Currently, much of the technology used to diagnose a traumatic brain injury, such as a concussion does not meet that requirement. MRI machines or simply shining a light at the patient’s eyes and observing the dilation are both capable of determining if someone has had a concussion. However, these solutions are expensive, time-consuming, require special training in order to accomplish, and definitely aren’t suited for a combat environment.
The team decided to create a software, capable of doing what previous solutions weren’t. Their goal was to be able to determine if someone had a concussion simply from a video. Using MATLAB, the team developed code for breaking down the video frame by frame, changing the color to black and white, and analyzing the size of the pupil as it was exposed to bright light. This way, the reaction time for the pupil’s dilation could be calculated, determining whether or not the person in question has a concussion. As for accessibility, the software could be developed to be used on a smartphone or tablet.
This development was not without its problems. Brown eyes often resulted in a lower contrast between the pupil and the rest of the eye, making it harder for the pupil to be identified. Furthermore, areas with different, more ambient lighting proved to be difficult when it came to finding the reaction time of the pupil.
While the tool is still in development and being perfected, it represents an important step towards recognizing and treating brain injuries as soon as they happen. Having a versatile and easily available tool for identifying concussions would be a huge benefit, not only in a combat situation but for the medical industry as a whole.
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