Table 1_Real-world feasibility of privacy-preserving, non-wearable AI for real-time fall detection with disease-specific video classification in parkinsonian syndromes: a proof-of-concept clinical study.docx

We evaluated a privacy-preserving, non-wearable AI system for real-time fall detection and exploratory video analysis in Parkinsonian syndromes.</p>Methods<p>Residents of a PD-specialized facility were continuously monitored for 22–63 days using a ceiling-mounted depth camera with in-sensor edge AI.

All video-confirmed falls were reviewed by two blinded raters and classified into 10 predefined operational categories. Detection performance was summarized descriptively, and fall characteristics were explored using chi-square tests with false-discovery-rate control.</p>Results<p>Across 6,272 h of monitoring, 74 real-world falls were confirmed. AI sensitivity was 52.7% across the full study and 67.6% during the post-update evaluation phase, whereas conventional facility reporting identified 28.4% and 29.4% of video-confirmed falls, respectively.