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AI-Driven Fall Detection Research

PythonComputer VisionMachine LearningActionCLIPMMAction2

Designed and built a semi-automated video annotation pipeline using ActionCLIP and MMAction2 to support fall detection research in elderly care. Developed scripts for video segmentation, zero-shot inference, and evaluation across 5,000+ videos. Assisted in ELAN tool adoption and ontology design for ground-truth labelling. Improved model prediction accuracy through prompt engineering and simplified label structuring.