To what extent can a sensorless, imaging-based AI system accurately identify biomechanical risk factors for ACL injuries while serving as an accessible feedback tool for everyday athletes?
Project by Polygence alum Omer
Project's result
Created an AI-powered app that provides feedback to individuals who submit images of themselves in the middle of an athletic movement to determine whether they embody a risk to ACL injuries. I also wrote a research paper on biomechanics and the use of a sensorless approach to analyzing sports movements and predicting injury risks.
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Summary
The intersection of biomechanics, artificial intelligence, and applied mathematics is transforming how we prevent ACL and lower-body injuries. Rather than relying on expensive lab tools or wearable sensors, athletes can now receive accurate feedback through imaging-based motion analysis powered by AI and physics. These systems go beyond forming skeletal models and mirroring the person’s movement by now analyzing internal forces like torque and joint load to detect injury risk immediately. As studies have shown, this sensorless approach is both more reliable and accessible, paving the way for solutions that bring proactive injury prevention to athletes at all levels, anywhere and anytime.
Baxter
Polygence mentor
PhD Doctor of Philosophy candidate
Subjects
Chemistry, Psychology, Business, Engineering, Computer Science
Expertise
Mental Health, Material Science, Design, Consulting, User Interface, User Experience
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Omer
Student
Graduation Year
2026
Project review
“Mentor helped when needed and was supportive throughout the journey. Project was a long process, but I expected that.”
About my mentor
“I would totally recommend Baxter. He will never fail to amaze you and will help in every step along your journey.”
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