Title: Automatic Sensing System to Detect Falls and Fall-Risk in Elders. Project Summary and Abstract One in every three people age 65 or older falls each year, making falls the most common cause of injuries and hospitalizations for trauma in older adults and the leading cause of death due to injury (CDC, 2006). Researchers have studied falls, fall risk assessment, and interventions to prevent falls. However, to date, their methods require that research staff or clinicians complete multi-factorial assessments of fall risk and that research subjects maintain logs of falls, wear devices that measure changes in positions that could indicate a fall or activate an alarm that they need assistance. Building on our current work, we propose to validate and deploy an innovative technological approach that automatically detects when falls have occurred or when the risk of falls is increasing. Subjects will not have to press buttons, pull cords or wear any devices. This new "passive" approach using sensors in the home could revolutionize detecting and preventing falls as well as measuring fall risk. By detecting falls or increasing fall risk early, this new technology can act as a trigger for elders, family members, or health care providers to improve physical function or better manage illnesses that are precipitating falls. The products of this study can improve access to fall risk measures by deploying the new sensor system in any private house or apartment as well as senior centers, churches, or retail stores. In such settings, persons could go to an accessible area to perform the guided motions to be measured by the sensor network developed in this application. In just a few minutes, a person would have a reliable fall risk indicator to alert increasing fall risk. An automatic sensing system to detect falls has major potential in senior housing, long-term care settings, private community housing as well in acute care settings where falls are a major risk. After laboratory validation using 567 falls performed by trained theater stunt actors, we will deploy the prototype sensing system for two years of data collection in ten apartments of elders in an independent living setting to complete validation and field testing (again using 960 falls performed by stunt actors). This application integrates the specialized talents and perspectives of not only health care scientific disciplines (nursing, physical therapy, social work, medicine) but also electrical and computer engineering, computer science, and informatics. This application will be of interest to AHRQ and likely the Innovations and Emerging Areas Portfolio that "will foster and nurture ideas and projects that have potential to lead to highly innovative solutions that may lead to significant advances in healthcare practice..."

Public Health Relevance

(3 sentence) relevance of research to public health Falls are the most common cause of injuries and hospitalizations for trauma in older adults and the leading cause of death due to injury (CDC, 2006). We propose to validate an innovative technological approach that automatically detects when falls have occurred or when the risk of falls is increasing. The products of this study will improve access to fall risk assessment and fall detection by deploying the new sensor system in any private house or apartment as well as senior centers, churches, or retail stores.

Agency
National Institute of Health (NIH)
Institute
Agency for Healthcare Research and Quality (AHRQ)
Type
Research Project (R01)
Project #
5R01HS018477-04
Application #
8281330
Study Section
Health Care Technology and Decision Science (HTDS)
Program Officer
Eldridge, Noel
Project Start
2009-09-30
Project End
2013-07-31
Budget Start
2012-08-01
Budget End
2013-07-31
Support Year
4
Fiscal Year
2012
Total Cost
Indirect Cost
Name
University of Missouri-Columbia
Department
Administration
Type
Schools of Medicine
DUNS #
153890272
City
Columbia
State
MO
Country
United States
Zip Code
65211
Wang, Fang; Skubic, Marjorie; Rantz, Marilyn et al. (2014) Quantitative gait measurement with pulse-Doppler radar for passive in-home gait assessment. IEEE Trans Biomed Eng 61:2434-43
Rantz, Marilyn J; Skubic, Marjorie; Abbott, Carmen et al. (2013) In-home fall risk assessment and detection sensor system. J Gerontol Nurs 39:18-22
Wang, Fang; Stone, Erik; Skubic, Marjorie et al. (2013) Toward a passive low-cost in-home gait assessment system for older adults. IEEE J Biomed Health Inform 17:346-55
Stone, Erik E; Skubic, Marjorie (2013) Unobtrusive, continuous, in-home gait measurement using the Microsoft Kinect. IEEE Trans Biomed Eng 60:2925-32