Published. Our paper “Physio at Home: Survey on AI Motion Tracking for Medical Recovery Exercises and…
Om S. — CyberSecurity Research Engineer
Published.
Our paper “Physio at Home: Survey on AI Motion Tracking for Medical Recovery Exercises and Suggestions Based on Accuracy” is now part of ICMSLE 2024, published by Springer (Algorithms for Intelligent Systems series).
In this work, we explored how AI-based motion tracking frameworks like MediaPipe, AlphaPose, OpenPose, and YOLOv7 can be used to improve recovery-focused medical exercises — especially when professional supervision isn’t always available.
Key focus areas:
• Motion accuracy in rehabilitation exercises
• Real-time feedback systems
• Pose correction for injury recovery
• AI-assisted progress tracking for patients
Recovery exercises are often underestimated — but incorrect execution can cause more harm than good. This research looks at how computer vision can bridge that gap.
Grateful to my co-authors and mentors for the collaboration.
DOI: https://link.springer.com/chapter/10.1007/978-981-97-1488-9_45
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