Fall Detection and Prevention for Memory Care Through Real-time Artificial Intelligence Applied to Video

Fall Detection and Prevention for Memory Care Through Real-time Artificial Intelligence Applied to Video

Fall Detection and Prevention for Memory Care Through Real-time Artificial Intelligence Applied to Video: A Randomized Control Trial

The purpose of the research is to study a new safety monitoring system developed by SafelyYou to help care for a loved one with dementia. The goal is to provide better support for unwitnessed falls. The SafelyYou system is based on AI-enabled cameras which detect fall related events and upload video only when these events are detected. The addition of a Human in the Loop (HIL) will alert the facility staff when an event is detected by the system.

This process enables staff to know about falls without requiring residents wear a device and to see how falls occur for residents that cannot advocate for themselves while still protecting resident privacy by only uploading video when safety critical events are detected. Seeing how the resident went to the ground (1) prevents the need for emergency room visits when residents intentionally moved to the ground without risk and (2) allows the care team to determine what caused an event like a fall and what changes can be made to reduce risk. PRELIMINARY EVIDENCE. The proposed study follows a series of pilots. In pilot 1, we showed the technical feasibility of detecting falls from video with 200 falls acted out by healthy subjects. In pilot 2, in a 40-resident facility, we demonstrated the acceptance of privacy-safety tradeoffs and showed a reduction of total facility falls by 80% by providing the system for 10 repeat fallers. In pilot 3, we addressed repeatability of fall reduction in a cohort of 87 residents with ADRD in 11 facilities of three partner networks. In pilot 4 (NIH SBIR Phase I), we demonstrated that falls can be detected reliably in real-time within the partner facilities. We detected 93% of the falls; reduced the time on the ground by 42%; showed that when video was available, the likelihood of EMS visit was reduced by 50%; and reduced total facility falls by 38%.

Detection of falls will be performed with blurred video, hence with increased privacy. Expected outcome will be the change in enrollment rate compared to previous feasibility studies (i.e. impacted rate of positive responses to recruitment efforts within facilities).

Care staff will be alerted as soon as the transition is detected (intervention of the front line staff). This may produce an immediate reduction in falls due to this type of transition.

As the system learns to may produce an immediate impact on the fall rate by intervention of the front-line staff when the change is detected.

The study population includes residents of care facilities that are a high fall risk with a particular focus on care facilities with high populations of individuals with Alzheimer's disease and related dementias. There are no gender, race, ethnicity, language or literacy requirements for participation and all residents are eligible. Inclusion criteria - Living at a participating skilled nursing facility or equivalent, CCRC, Exclusion criteria - 18 years old or younger