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Using Structured Light Sensing with Machine Learning to Detect Unwitnessed In-Home Falls

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 2R44AG066263-02A1
Agency Tracking Number: R44AG066263
Amount: $768,585.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: NIA
Solicitation Number: PAS22-196
Timeline
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-09-30
Award End Date (Contract End Date): 2026-08-31
Small Business Information
23875 CHESTNUT DR
Loretto, MN 55357-9536
United States
DUNS: 080808144
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 PAUL GIBSON
 (612) 470-9532
 paul@audynamics.com
Business Contact
 PAUL GIBSON
Phone: (612) 470-9532
Email: paul@audynamics.com
Research Institution
N/A
Abstract

Project Summary/Abstract
Older adults are disproportionately affected by falls. Older adults who have memory loss (mild to moderate
cognitive impairment) can forget to wear wireless alert pendants or wristbands that are used in case they fall in
their home. Falls among adults 65 and older caused over 34,000 deaths in 2019, making it the leading cause
of injury death for that group. Older adult falls cost $50 billion in medical costs annually. Of those who fall,
many suffer serious injuries, such as hip fractures and head traumas, which reduces their mobility,
independence, and life expectancy. Studies have found an increased risk of complications associated with
prolonged periods of lying on the floor following a fall. Older adults living alone or with memory loss are at the
greatest risk of delayed assistance following a fall and cannot always be counted on to use their wearable
emergency alert button. A low-cost, unobtrusive system capable of automatically detecting and alerting falls in
the homes of older adults living alone or those with mild to moderate cognitive impairment, could help
significantly reduce the incidence of delayed assistance after a fall.
This phase II project, building on a successful phase I project, will develop an innovative new in-home fall
monitoring system that solves many practical problems with existing systems. The technical approach uses
structured light sensing (SLS) that creates 3D point clouds of a scene to allow detection of motion sequences
using machine learning (ML) algorithms which will allow for the automatic detection of a person’s fall. There are
multiple benefits of this approach for the target users: 1. The person is not required to carry or wear an
electronic device that might be forgotten to be worn. 2. No action is required to be taken by the person after a
fall. 3. The system does not use visible light video that would create privacy concerns for the person. 4. The
system can work in darkness or very low light unlike visible light camera-based approaches. 5. The system is
unobtrusive and works with existing Personal Emergency Response Systems (PERS), with minimal or no
active user interaction.
The SLS fall detection system is intended to work with multiple vendors of in-home alert systems. It will operate
in lieu of or in parallel with, wearable buttons to signal an alert. The proposed system would be used if
caregivers determine that a wearable button is not an adequate solution for the person being monitored. The
proposed devices will be mounted high on the wall of each room and will wirelessly communicate to a central
device in the home. The central device will send the alert to the in-home alert system upon detecting a fall. The
proposed solution will not require any Internet connectivity. The out-of-home communication method is
provided by the chosen vendor of the in-home alert system.

* Information listed above is at the time of submission. *

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