Abstract In the US, Alzheimer’s disease (AD) is the single most expensive disease, the only disease in the top six for which the number of deaths is increasing. The greatest cost contributors are frequent hospitalizations, where falls are the largest culprit, and frequent need for assistance with the activities of daily living. Fall safety systems show the potential to reduce costs and increase quality of care by reducing the likelihood of emergency events (e.g., detecting falls before a fracture occurs and reducing the number of repeat falls). Unfortunately, current safety devices require wearable or sensor technology not suitable for individuals with dementia and incapable of showing caregivers how falls occur. Our goal is to extend the impacts of SafelyYou Guardian, an online fall detection system with off-the- shelf wall-mounted cameras to automatically detect falls for patients with AD and related dementias (ADRD), enabled by a human-in-the-loop (HIL). The HIL, who can monitor several facilities at a time, confirms the fall detection alerts provided by our artificial intelligence algorithms, and places a call to the facilities, so an intervention can happen within minutes of the fall detection (as opposed to hours after, when the next scheduled visit to the room of the patient happens). Subsequently, an Occupational Therapist (OT) reviews the fall videos to make recommendations on how to re-organize the patient space (intervention), to prevent future falls. We leverage our HIL paradigm, in which our deep learning (a subfield of artificial intelligence) approaches identify and pre-filter falls well enough to leave the last check to a human, who will call the facilities in case of detected safety critical events (falls). The present Fast Track NIH SBIR project leverages the already recruited 100 patients in our partner 11 memory care facilities, recruited through our previous IRB-approved pilots, which we leverage: · Pilot 1: We demonstrated the feasibility of the system by collecting proof-of-concept data containing 200 acted falls of healthy subjects and showed accurate fall detection. · Pilot 2: We demonstrated acceptance of privacy/safety tradeoffs by patients, family and staff, through the collection of 3 months of video data at WindChime of Marin, a memory care facility from the Integral Senior Living network, in which we identified 4 total hours of fall data. This led to clinical benefits including an 80% fall reduction through the intervention of an Occupational Therapist (OT) to re-organize patient space. · Pilot 3: We demonstrated scalability and acceptance by deploying the system in 11 facilities, totaling 100 patients monitored by our system (offline, no HIL intervention). · Pilot 4 (ongoing): We demonstrated the ability to perform online (real-time) fall detection, with real-time intervention of the HIL through our partner company Magellan-Solutions, which provides the 24/7 monitoring service for the facilities, and confi...