Inspiration
Falls can pose serious safety risks, especially for older adults and individuals who may need assistance after an incident. Fall Guardian explores how computer vision can help identify potential falls from video and support a controlled response workflow.
What It Does
Fall Guardian is a computer vision prototype that analyzes video to identify movement patterns that may indicate a fall. It uses OpenCV-based motion analysis and rule-based decision logic, alongside a human-approval workflow for controlled event handling.
How We Built It
The prototype uses Python and OpenCV to analyze video frames. Its vision pipeline examines foreground movement, contours, bounding-box characteristics, and centroid movement. These features feed into rule-based logic to identify potential fall events. A separate workflow demonstrates human approval before proceeding with a simulated event-handling action.
Challenges We Ran Into
One key challenge was distinguishing potential falls from ordinary movements, particularly actions such as intentionally lying down. Another challenge was evaluating the decision logic and understanding how false positives and missed detections affect reliability. We also focused on making the workflow transparent and distinguishing simulated operations from real-world integrations.
Accomplishments That We're Proud Of
We developed a working computer vision prototype with a structured detection pipeline, evaluation tests, and a human-approval workflow demonstration. In our recorded evaluation on a small synthetic dataset, the system correctly classified 14 of 16 clips, achieving 87.5% accuracy. These results provide an initial benchmark for further improvement.
What We Learned
This project strengthened our understanding of computer vision, motion analysis, rule-based decision-making, software testing, and evaluation metrics. We also learned the importance of human oversight, reproducible testing, and transparently communicating limitations in safety-related applications.
What's Next for Fall Guardian
Future improvements include testing on larger and more diverse datasets, reducing false alarms, improving detection robustness across different environments, and implementing and verifying production-ready integrations. Further validation will be necessary before considering real-world safety applications.
Evaluation and Limitations
The current evaluation uses a small synthetic video dataset and is not representative of real-world clinical performance. The recorded evaluation included one false positive and one false negative, demonstrating that the system can incorrectly flag non-falls or miss potential falls. Fall Guardian is an experimental prototype, not a clinically validated safety system. Simulated workflow events must not be interpreted as real emergency notifications or verified cloud deployment.
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