# Mitigating Injurious Falls in Older Adults Through Non-Injurious Fall and Gait Analysis From Floor Vibrations

> **NIH NIH R42** · ADVANCED SMART SYSTEMS/EVALUATION TECHNO · 2021 · $740,205

## Abstract

Project Summary and Abstract
Falls are the leading cause of death due to injury. Falls are so common that 30% of community dwelling older
adults, and 50% of residents in Care Facilities will experience a fall in the coming year. The risk of falling
substantially increases for those having Alzheimer’s disease and related dementias. The financial burden is
significant with fall-related costs being $50 billion. Care Facilities, who are often liable for the well-being of their
patients, bear a substantial portion of the cost. A fall can cost $10,484 per case for Care Facilities.
Commercially available fall detection systems operate via wearable pendant-based devices that patients press
after experiencing a fall. Newer generations of these systems also incorporate accelerometers that are
reportedly able to detect falls. These systems are patient-dependent, meaning that a patient must be wearing
the pendant for it to work which older adults, particularly those with cognitive impairments, often do not.
Furthermore, the patient has to be cognizant to press the button to call for aid if the pendant does not activate
during a fall. This is unlikely to occur as even when people are not cognitively impaired, they will only activate
the system 20% of the time.
There is a clear need for an automated, patient-independent fall detection system to fill the gaps left by current
approaches. Better yet would be a system that can detect non-injurious falls or changes in gait parameters,
both of which are predictors of oncoming injurious falls. ASSET, in partnership with the University of South
Carolina, has developed a patented, floor vibration monitoring system that can detect falls and collect gait
information whilst being patient independent. The innovative product has the ability to firmly place control of
liability back into the hands of Care Facilities much like what a fire alarm does for property damage from fires,
and potentially saving ~$2.2 billion in fall-related costs with just 5% market adoption.
During Phase II our overall goals are two-fold, first to further develop a system that does not rely on the patient
to operate, overcoming the limitation of wearable systems and can additionally capture falls that are a predictor
of oncoming injurious falls. We will monitor common areas with our vibration sensor system in places where
Care Facility staff report the majority of falls occur. To accomplish the methods, we will use the Care Facilities’
common area video camera system to corroborate sensor fall activations are actual falls. Second, we will use
the same passive system technology to explore gait measurement as an additional indicator of an oncoming
health changes such as a fall. We will use gait parameter measuring technology in a Care Facility medical
office for regular vital monitoring. We will use gait measurements with Facility fall reports to explore the
effectiveness of our predictive fall risk model against industry-standard fall risk assessment...

## Key facts

- **NIH application ID:** 10383468
- **Project number:** 2R42AG059475-02A1
- **Recipient organization:** ADVANCED SMART SYSTEMS/EVALUATION TECHNO
- **Principal Investigator:** Stacy Lynne Fritz
- **Activity code:** R42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $740,205
- **Award type:** 2
- **Project period:** 2018-09-30 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10383468

## Citation

> US National Institutes of Health, RePORTER application 10383468, Mitigating Injurious Falls in Older Adults Through Non-Injurious Fall and Gait Analysis From Floor Vibrations (2R42AG059475-02A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10383468. Licensed CC0.

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