Autonomous Patient Safety Assessment from Depth Camera Based Video Analysis

We introduce a low-cost, minimally intrusive system for the detection of high-risk postures and movements for patients. Our current focus is on the detections of when a patient leaves the bed surface and when a patient moves their body, which could be potentially utilized to detect a fall from bed, pressure ulcer, and tonic-clonic seizure risks in real-time. Using simulated data from healthy individuals, we develop and assess initial detection algorithms