My Fall-Detection Model Scored 94%, and It Was Lying to Me

A machine learning model for fall detection initially boasted a 94% accuracy score, but this impressive result was discovered to be deeply misleading. The inflated score was caused by data leakage, where randomly splitting video frames for testing allowed the model to simply memorize data rather than learn to generalize to new situations. Correctly splitting the data by entire videos revealed the model's true accuracy was a much lower 69%, prompting a complete project overhaul. This rebuild involved meticulously relabeling the data based on the exact moment of a fall and shifting evaluation to event-level metrics, such as alarm latency and false alarm rates. Ultimately, this honest approach uncovered a critical design trade-off between a model that detects more falls but is prone to false alarms and one that is more precise but misses some events.