The Quality Improvement Program for Missouri's Long - Term Care Facilities (QIPMO) is committed to Missouri's Elderly.
The "Aging-in-place" model allows older adults to receive health care in their preferred place of living, eliminating the need for a more restricted living space, such as a nursing home.
TigerPlace is a specially designed elder housing project initiated by the MU Sinclair School of Nursing, working to provide elders a better quality of life.
Interdisciplinary research is a major focus for MU faculty and many have come together to focus their research expertise on improving the lives of older people. There are several large research projects funded by the National Institutes of Health (NIH), National Science Foundation (NSF), and Agency for Healthcare Research and Quality (AHRQ) underway developing and applying technology to help the residents of TigerPlace age in place. Research teams are pursuing multiple ways to measure physical function, detect falls, and early illness recognition. Grant proposals to NIH, NSF, AHRQ, and other funding agencies are continuously under development with PIs from our multidisciplinary team.
Falls are a major problem in older adults. A continuous, unobtrusive, environmentally mounted (i.e., embedded into the environment and not worn by the individual), in home monitoring system that automatically detects when falls have occurred or when the risk of falling is increasing could alert health care providers and family members to intervene to improve physical function or manage illnesses that may precipitate falls. Researchers at the University of Missouri Center for Eldercare and Rehabilitation Technology are testing such sensor systems for fall risk assessment (FRA) and detection in older adults’ apartments in a senior living community. Initial results comparing ground truth (validated measures) of FRA data and GAITRite System parameters with data captured from Microsoft® Kinect and pulse-Doppler radar are reported.
Passive sensor networks were deployed in independent living apartments to monitor older adults in their home environments to detect signs of impending illness and alert clinicians so they can intervene and prevent or delay significant changes in health or functional status. A retrospective qualitative analysis was undertaken to refine health alerts to improve clinical relevance to clinicians as they use alerts in their normal work flow of routine care delivery to older adults. Clinicians completed text boxes to describe actions taken (or not) as a result of each alert. The significance for each health alert was also rated on a scale of 1-5. Two samples for analysis after alert algorithms had been adjusted based on results of a pilot study using health alerts.
As people age, they want to remain as active and independent as possible for as long as possible. They want to age at home, not in institutions like nursing homes (Marek & Rantz, 2000). According to a 2010 AARP survey, 88 percent of people over age 65 want to stay in their residence for as long as possible (AARP, 2010). Technology has the potential to help people remain at home by monitoring their health status, detecting emergency situations such as debilitating falls, and notifying health care providers to potential changes in health status or emergency situations. Researchers at the University of Missouri (MU) are using sensor technology at TigerPlace (a senior housing complex that enables residents to Age in Place) to detect changes in health status of the residents, alert health care providers, and augment traditional healthcare. This article reviews the Aging in Place research, TigerPlace as a state sponsored Aging in Place site, and the sensor technology developed by MU to support Aging in Place.
In this paper, we propose a webcam-based system for in-home gait assessment of older adults. A methodology has been developed to extract gait parameters including walking speed, step time and step length from a three-dimensional voxel reconstruction, which is built from two calibrated webcam views. The gait parameters are validated with a GAITRite mat and a Vicon motion capture system in the lab with 13 participants and 44 tests, and again with GAITRite for 8 older adults in senior housing. An excellent agreement with intra-class correlation coefficients of 0.99 and repeatability coefficients between 0.7% and 6.6% was found for walking speed, step time and step length given the limitation of frame rate and voxel resolution. The system was further tested with 10 seniors in a scripted scenario representing everyday activities in an unstructured environment. The system results demonstrate the capability of being used as a daily gait assessment tool for fall risk assessment and other medical applications. Furthermore, we found that residents displayed different gait patterns during their clinical GAITRite tests compared to the realistic scenario, namely a mean increase of 21% in walking speed, a mean decrease of 12% in step time, and a mean increase of 6% in step length. These findings provide support for continuous gait assessment in the home for capturing habitual gait.
We present an example of unobtrusive, continuous monitoring in the
home for the purpose of assessing early health changes. Sensors embedded in
the environment capture activity patterns. Changes in the activity patterns are
detected as potential signs of changing health. A simple alert algorithm has
been implemented to generate health alerts to clinicians in a senior housing
facility. Clinicians analyze each alert and provide a rating on the clinical
relevance. These ratings are then used as ground truth in developing classifiers.
Here, we present the methodology and results for two classification approaches
using embedded sensor data and health alert ratings collected on 21 seniors over
nine months. The results show similar performance for the two techniques,
where one approach uses only domain knowledge and the second uses
supervised learning for training.
The purpose of the study is to investigate whether motion density maps based on passive infrared (PIR) motion sensors and the dis-similarity measure of the density maps, along with relative energy expenditure estimates derived from motion density are sensitive enough to detect changes in mental health over time.