Principal Investigator: Skubic

Customized Health Alerts and Consumer-Centered Interfaces for ADRD Patients and Family

Sensing technologies hold enormous potential for detecting and tracking health changes that can dramatically affect the aging experience. Embedded health assessment can improve management of chronic or acute conditions, and thus, improve quality of life. Problems in chronic disease management are often the cause of losing independence for aging Americans.

Physical Therapist Functional Assessment Tool

We are developing an application to automate data collection for the physical function assessments that are normally measured manually by physical therapists or nurses. The assessment tool will be used every 6 months to assess older adults’ functional movement and range of motion as part of the Health Kiosk project.

US Ignite: EAGER: GENI-Enabled In-Home, Personalized Health Monitoring and Coaching

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.

EAGER: An In-Home Health Alert System with Remote Care Coordination

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.

HCC: Human-Driven Spatial Language for Human-Robot Interaction

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.

Active Heterogeneous Sensing for Fall Detection and Fall Risk Assessment

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.

HCC: Elder-Centered Recognition Technology for the Assessment of Physical Function

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.

Technology Interventions for Elders with Mobility and Cognitive Impairments

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.