ACL injuries may get an early-warning system through AI and wearable sensors
As a sophomore wide receiver who had just joined the USF football team, Jaden Alexis didn't expect a routine play to turn into a career-defining moment. During preseason football camp, Alexis had just sprinted up the sideline after catching a pass when a split-second collision changed everything.
A new early-warning system for ACL injuries is being developed through the use of artificial intelligence and wearable sensors, according to a recent study. Jaden Alexis, a sophomore wide receiver for the USF football team, experienced a career-defining ACL tear during preseason camp. The sudden, forceful collision resulted in the tearing of his right anterior cruciate ligament (ACL), a common knee injury that can occur without contact.
Alexis, who has since sustained multiple ACL injuries, emphasized the mental toll that such injuries take on athletes, as they must regain trust in their bodies. Dr. Nathan Schilaty, an associate professor at USF, and John Templeton, an assistant professor in AI, cybersecurity, and computing, aim to prevent ACL injuries by identifying risky movement patterns through AI-powered wearable technology.
Using a decade of research, they have developed machine learning models that can distinguish between prerupture and rupture conditions with high accuracy. These models, which analyze biomechanical variables, will help design sensors for wearable devices that provide real-time feedback to athletes, allowing them to self-correct before an injury occurs.
The goal is to move athletes away from risky movements, rather than predicting injuries with absolute certainty.
Written by urgent.news from Medical Xpress's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
