In the United States, 37% of the population is affected by cardiovascular related diseases. In order to avoid fatal consequences, in-home monitoring systems have been under development for the purpose of detecting early signs of cardiovascular abnormalities. Monitoring the heart rate and other cardiac parameters during sleep can provide critical information about the health of a subject.
We have tested our monitoring and health alert system in TigerPlace, an aging in place facility near the University of Missouri campus in Columbia, MO and, more recently, in assisted living in Cedar Falls, IA. The proposed project will build on this work with an innovative, interactive healthcare service. The monitoring system with health alerts will be introduced into independent housing in Kansas City. A new interactive exercise coaching interface will connect a remote physical therapist to senior clients in the home. GENI-enabled networking will be incorporated to support interactive monitoring and coaching that operates in real-time.
In this project, we test the concept in senior housing in Cedar Falls, Iowa, using in-home sensors and remote video conferencing for the nurse care coordination. Fiber networking in Columbia and Cedar Falls will provide the bandwidth and latency essential for this approach. Previous system development is utilized and a new hydraulic bed sensor has been integrated. The sensor configuration also includes the team’s previous work with the Kinect depth images for extracting gait parameters of residents in the home.
The main goal of this work was to develop algorithms for early illness recognition in elderly. Early illness recognition (EIR) is important, as research has shown that results in better medical outcomes and a reduction in health care cost. We developed methodologies (see Figure 1) that link sensor data to the medical (nursing) records for monitoring the residents of TigerPlace, an aging in place community from Columbia, Missouri.
Human subject experiments will be conducted with college students and elderly participants to explore spatial descriptions in a fetch task; results will drive the development of robot algorithms, which will be evaluated using a similar set of assessment experiments in virtual and physical environments.
We leverage ongoing research at a unique local eldercare facility (TigerPlace) to study active sensing and fusion using vision and acoustic sensors for the continuous assessment of a resident’s risk of falling as well as the reliable detection of falls in the home environment. The project investigates the interplay between fall detection and fall risk assessment.
Researchers at the University of Missouri-Columbia and the University of Washington have established a multidisciplinary team comprised of researchers in computer science and engineering, nursing, and medical informatics dedicated to developing and evaluating technology to keep older adults functioning at higher levels and living independently. We have leveraged ongoing research at a unique local eldercare facility (TigerPlace) to study vision-based recognition methods for multi-person environments designed to capture continuous and automated assessments of older adults’ physical function.
Americans are living longer and more fulfilled lives, and they desire to live as independently as possible. But independent lifestyles come with risks. To address these issues, researchers are developing “smart home” technologies to help older adults remain independent at home while controlling costs. Smart homes enhance residents’ safety and monitor health conditions using sensors and other devices. Such technology can help keep older adults independent while controlling costs.