Fall Detection and Prevention for Memory Care Through Real-time Artificial Intelligence Applied to Video
NCT ID: NCT03685240
Last Updated: 2022-05-09
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
NA
460 participants
INTERVENTIONAL
2023-10-31
2023-12-31
Brief Summary
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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.
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Detailed Description
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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%.
Conditions
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Study Design
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RANDOMIZED
PARALLEL
SUPPORTIVE_CARE
DOUBLE
Study Groups
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Intervention
AI-enabled camera fall detection with Human-in-the-Loop (HIP) review
SafelyYou Fall Prevention System
Technology + Quality Assurance Services Provided by SafelyYou
Control
No camera detection
No interventions assigned to this group
Interventions
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SafelyYou Fall Prevention System
Technology + Quality Assurance Services Provided by SafelyYou
Eligibility Criteria
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Exclusion Criteria
18 Years
ALL
Yes
Sponsors
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National Institute on Aging (NIA)
NIH
SafelyYou
INDUSTRY
Responsible Party
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Locations
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SafelyYou
San Francisco, California, United States
Countries
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Central Contacts
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Facility Contacts
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Provided Documents
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Document Type: Study Protocol, Statistical Analysis Plan, and Informed Consent Form
Other Identifiers
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SY-NIA-GX001
Identifier Type: -
Identifier Source: org_study_id
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