Sensing technologies hold enormous potential for early detection of health changes that can dramatically affect the aging experience. Embedded health assessment can enable functional independence, improve self-management of chronic or acute conditions, and thus, improve quality of life
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.
Extracting information from the sensors installed in the homes of elderly pose a unique set of challenges. Add to it the short amount of time the clinicians and nurses have to analyze this data, and the problem becomes more complicated. The ongoing work in this project focuses on development of algorithms to glean information from in-home sensor data and then presenting it in the form of textual summaries using Natural Language Generation techniques.
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.
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.
We propose a carpet with pressure sensors distributed throughout the floor with an average of 10 sensors per square foot sheet; these will be incorporated onto flooring material such as carpeting, flexible tiles or linoleum. We will be able to “see” the person’s footsteps, assess their gait, and identify their location.
Our objective is to explore new information technologies to assist the independent living of elderly people and enhance their quality of life at home, while utilizing the time and attention of caregivers and eldercare specialists in the highest efficiency.