INTELLIGENT VISION-BASED FALL DETECTION SYSTEM: PRELIMINARY RESULTS FROM A REAL-WORLD DEPLOYMENT

An automated, vision-based, ceilingmounted Personal Emergency Response System has been developed to detect falls in real home environments. The system employs visual background modeling which separates a subject’s shadowed silhouette and shadow-less silhouette regions. Analysis of these regions is performed to create velocity, area, and moment features. Machine learning then classifies these features to detect “fall” vs. “non-fall” events from the video input. In-home tests were conducted to verify the system’s ability to operate in real world environments. During a seven day trial, all of the 11 simulated falls were successfully detected with 5.4 false alarms per day.

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