Tampa, FL – A wearable system being developed at the University of South Florida could help athletes identify dangerous movements before they result in a serious knee injury.
USF researchers are combining artificial intelligence, biomechanics and motion analysis to create a knee sleeve designed to detect movement patterns associated with anterior cruciate ligament tears.
The ACL connects the thigh bone to the shin bone and helps stabilize the knee.
Injuries often occur during sudden stops, changes in direction or awkward landings.
Dr. Nathan Schilaty, an associate professor at the USF Health Morsani College of Medicine and Department of Medical Engineering, said approximately 75% to 80% of ACL injuries occur without direct contact.
Schilaty and John Templeton, an assistant professor in USF’s Bellini College of Artificial Intelligence, Cybersecurity and Computing, are developing the technology using human motion research and computer modeling.
Their machine learning system analyzes several biomechanical variables simultaneously to identify patterns linked to increased injury risk.
Researchers reported 86% accuracy in distinguishing conditions before a rupture from those during an ACL tear.
The system reached 90% accuracy when identifying conditions immediately preceding a rupture.
The proposed sleeve would use sensors to track an athlete’s movements and issue a warning, potentially through vibration, when it detects a high risk position.
The alert could give the wearer time to adjust without constant supervision.
Researchers said the technology cannot predict or prevent every ACL tear.
Its purpose is to identify risky movement patterns and encourage correction.
ACL surgery can cost between $20,000 and $40,000 and require one year of recovery.
Excite Medical has licensed the technology as researchers pursue funding, testing and commercialization.