# Fall Detection and Prevention for Memory Care through Real-Time Artificial Intelligence Applied to Video

> **NIH NIH R44** · SAFELYYOU, INC. · 2020 · $493,633

## Abstract

Abstract
In the US, Alzheimer’s disease (AD) is the single most expensive disease, the only one in the
top six for which the number of deaths is increasing. The greatest costs are hospitalizations,
where falls are the largest culprit, and frequent need for assistance with daily life activities. 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, reducing the
number of repeat falls). Unfortunately, no fall detection and prevention technology has been
developed specifically for the needs of dementia care where individuals (1) fall more frequently
and (2) often cannot tell care staff how they fell, leading to increased use of Emergency Medical
Services (EMS) when falls are unwitnessed to ensure affected individuals are safe.
Our goal is to perform a randomized wait-list control clinical trial (n=460) of SafelyYou Guardian,
an online fall detection system with wall-mounted cameras to automatically detect falls for
residents with AD and related dementias (ADRD). The automation is based on algorithms that
push the frontier of deep learning, a subfield of Artificial Intelligence (AI), with a human-in-the-
loop (HIL). SafelyYou Guardian is designed to primarily operate in memory care facilities
(defined herein as assisted living and skilled nursing facilities providing ADRD care). Deep
learning has already revolutionized several fields: robotics, self-driving cars, social networks in
particular. Our approach is anchored in novel algorithms developed at the Berkeley AI Research
Lab (BAIR) and extended by SafelyYou for real-time detection of rare events in video. The HIL
is operating from a call center, confirms the fall detection alerts provided by our artificial
intelligence algorithms, and places a call to the communities, so an intervention can happen
within minutes of the fall detection. Subsequently, an Occupational Therapist (OT) working from
our office in San Francisco reviews the fall videos with the front-line staff over video conference
and using our web portal to make recommendations on how to re-organize the resident space
(intervention) to prevent future falls. We leverage our HIL paradigm, in which our deep learning
approach identifies and pre-filters falls with high sensitivity followed by a human who confirms
the fall with high specificity and calls the communities in case of detected fall. This project
leverages past small scale clinical and technical pilots including 87 residents from 11 partner
communities, and our experience with paid commitments for 480 residents from three partner
networks. Past pilots leading to this NIH Phase II proposal include:
 · Pilot 1: Technical proof of concept with healthy subjects (200 acted falls).
 · Pilot 2: We demonstrated acceptance of privacy/safety tradeoffs by residents, family and
 staff, through the collection of 3 months of video data at WindChime of Marin, our first
...

## Key facts

- **NIH application ID:** 10020322
- **Project number:** 5R44AG058354-03
- **Recipient organization:** SAFELYYOU, INC.
- **Principal Investigator:** Glen Xiong
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $493,633
- **Award type:** 5
- **Project period:** 2017-09-30 → 2021-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10020322

## Citation

> US National Institutes of Health, RePORTER application 10020322, Fall Detection and Prevention for Memory Care through Real-Time Artificial Intelligence Applied to Video (5R44AG058354-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10020322. Licensed CC0.

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