Summary Points
- A model initially scored 94.3% accuracy due to data leakage—frames from the same video were split randomly, causing the model to memorize rather than generalize, dropping to 69% accuracy after proper grouping.
- The training labels were inaccurate, with many “fall” labels misapplied to normal activity; real annotations revealed the model was solving an easier, unintended problem.
- After fixing the data split and labeling issues, the system achieved high event-level detection rates (~96%) with minimal latency, though some misses occurred due to pose estimation failures.
- The author emphasizes rigorous evaluation by grouping data, verifying labels, measuring event-level performance, and openly sharing all code and demos to build trust and accuracy in real-world applications.
The Illusion of Accuracy and Its Hidden Flaws
Initially, I was proud of my fall-detection model scoring 94.3% accuracy. I double-checked the results multiple times, and the numbers seemed solid. I documented the high score in my README and added it to my CV. However, the true story was different. Later, I discovered the real accuracy was only 69%. This mistake wasn’t a minor error; it was a major data leak issue. The problem stemmed from how I split my data for testing. I randomly divided frames from the same video, which made the test set almost identical to the training set. Consequently, the model was not tested on new, unseen videos. Instead, it had memorized specific frames. This situation shows how easy it is to be misled by seemingly good numbers. Honest evaluation requires looking beyond the surface and understanding how data is split and labeled.
Rechecking Labels and Gathering Better Data
After fixing the data split, I looked closer at my labels. I used folder names to set labels, but that was flawed. A staged fall only lasts a few seconds; the rest of the video shows normal activity. Cross-referencing with exact fall annotations revealed most “fall” labels were wrong. In fact, 76% of frames labeled as falls showed people walking or lying down. Only 6% showed real falls. This meant I was training my model on incorrect labels. The labels changed what the model learned to recognize. I rebuilt my dataset carefully from trusted annotations, including hand-annotated videos. I also added velocity features, capturing movement, so the model better understood actual falls. This process improved the evaluation, making it more truthful about the system’s real capabilities and limitations.
Measuring Real-World Performance and Practical Outcomes
After cleaning the data, the model’s true performance improved, but not to perfect levels. Importantly, I started evaluating the system based on real-life use scenarios. Instead of just looking at frame accuracy, I measured whether it correctly detected actual falls quickly and without false alarms. For example, the system identified 96% of falls with an alarm latency of just over half a second. It also minimized false alarms, alerting caregivers only when needed. During live testing, I deliberately fell for demonstration: the system responded reliably from start to finish. This hands-on testing gave me confidence that my system can work in real situations. It also highlighted that models must be part of a broader system — with reliable sensors, privacy considerations, and user-friendly alerts — to be effective and trustworthy.
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