Real time video monitoring of falls in memory care facilities for individuals with Alzheimer s and related dementias

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 A fall safety system shows 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 deploy and demonstrate NestSense also known as SafelyYou an online fall detection system with off the shelf wall mounted cameras to passively detect falls for patients with AD and related dementias ADRD enabled by a human in the loop HIL The HIL will confirm the fall detection alerts provided by our artificial intelligence algorithms We will demonstrate it for patients in memory care facilities Memory care facilities can select parameters that matter for specific patients for ex some patients wake up frequently during the night while others should be assisted when they attempt to leave the bed at night It does not require action of individuals caregivers such as wearing a fall pendant and is therefore well suited for individuals with ADRD 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 human can monitor several facilities at a given time This project leverages the already recruited patients in our partner memory care facilities recruited through our previous IRB approved pilot The work will leverage our previous three pilots Pilot We demonstrated the feasibility of the system by collecting a proof of concept data containing acted falls of healthy subjects and showed accurate fall detection Pilot We demonstrated acceptance of privacy safety tradeoffs by patients family and staff through the collection of months of video data at WindChime of Marin a memory care facility from the Integral Senior Living network in which we identified total hours of fall data This led to clinical benefits including a reduction of falls from and in the first months to in the final month due to video review with care staff Pilot ongoing We demonstrated scalability and further acceptance by deploying the system in facilities of the Carlton Integral Senior Living Pacifica and SRG networks totaling patients already monitored by our system offline The pilot proposed for this SBIR Phase I will translate the cameras in these facilities into a real time fall detection system which will run online for months with a HIL support Compared to a month baseline from the cameras recording with the detection offline we hypothesize this real time detection system will lead to a statistically significant reduction in time on the ground after a fall fall related hospitalizations and length of hospital stay following fall incidents based on results described in previous clinical trials with participants The proposed NestSense system uses off the shelf wall mounted cameras to perform detection of safety critical events for Alzheimer s disease and related dementia patients in memory care facilities It does not rely on active use from the patients or caregivers e g through wearing any kind of device The NestSense technology provides the first robust non wearable fall detection tuned specifically to the privacy security tradeoffs of dementia care It provides an answer to today s technology gaps far from reliability by use of a Human in the Loop HIL paradigm to enable a quick rollout of the technology Following the acceptance of privacy safety tradeoffs of our system by patients family and memory care staff in the Carlton Integral Senior Living Pacifica and SRG networks we have deployed the system in offline mode in facilities of the network monitoring patients The research implementation and deployment work encompassed in this SBIR Phase I will provide validation that the existing prototype can be rolled out to a proof of concept operational in real time mode with HIL to reduce fall related hospitalizations and time spent on the ground The pilot follows previous pilots the most recent of which deploys cameras with these patients in offline mode to study the effect on the fall rate of occupational therapist review of fall video in memory care This study will translate those cameras into a real time fall online detection system which will run online for months with a HIL support Compared to a month baseline from the cameras recording with the detection offline we hypothesize this real time detection system will lead to a statistically significant reduction in time on the ground after a fall fall related hospitalizations and length of hospital stay following fall incidents based on previous clinical trials with participants