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

S

SafelyYou

Status

Unknown

Conditions

Fall Injury
Alzheimer's Disease and Related Dementia

Treatments

Behavioral: SafelyYou Fall Prevention System

Study type

Interventional

Funder types

Industry
NIH

Identifiers

NCT03685240
SY-NIA-GX001

Details and patient eligibility

About

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.

Full description

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%.

Enrollment

460 estimated patients

Sex

All

Ages

18+ years old

Volunteers

Accepts Healthy Volunteers

Inclusion and exclusion criteria

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

Trial design

Primary purpose

Supportive Care

Allocation

Randomized

Interventional model

Parallel Assignment

Masking

Double Blind

460 participants in 2 patient groups

Intervention
Experimental group
Description:
AI-enabled camera fall detection with Human-in-the-Loop (HIP) review
Treatment:
Behavioral: SafelyYou Fall Prevention System
Control
No Intervention group
Description:
No camera detection

Trial documents
1

Trial contacts and locations

1

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Central trial contact

Glen Xiong, MD

Data sourced from clinicaltrials.gov

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