The Center for Eldercare and Rehabilitation Technology (CERT) includes an interdisciplinary group of faculty, staff, and students who are focused on investigating, developing, and evaluating technology to serve the needs of older adults and others with physical and cognitive challenges. This diverse group represents Electrical and Computer Engineering, Computer Science, Nursing, Medicine, Social Work, Physical Therapy, and Health Informatics.
Fig. 1. The TigerPlace Aging-in-Place Facility
Aging in Place and Eldertech Research at MU
Many senior citizens and their families, preferring to remain at home, want to postpone or even avoid nursing home care. The Aging in Place (AIP) project vision was developed in 1996 in the Sinclair School of Nursing with an interdisciplinary team to provide more and higher-quality services at home, allowing people to “age in place.” People get services when they need them, regain independence, and then services are limited or withdrawn so costs are controlled. Out of this initiative came TigerPlace, built by Americare Systems, Inc.: a state of the art independent living facility, built to nursing home standards, licensed as intermediate care so people can use long term care insurances, and operated as independent housing with services.
Grants led by principal investigators from the interdisciplinary team, totaling over $10 million, include funding from the National Institutes of Health, the National Science Foundation, the Agency for Health Care Research and Quality, the Administration on Aging, the Alzheimer’s Association, RAND Health, the Gerontological Nursing Interventions Research Center, and the Health Care Financing Administration.
Early Interventions through Nursing Care Coordination
By 2030, one in every five Americans will be 65 or older, growing from 35 million in 2010 to 71.5 million in 2030.1 Most older adults also have one or more chronic health conditions that require self-management or assistance in managing, and more than 40% need assistance with one or more activities of daily living.1 RN care coordination, health promotion, and early illness recognition and interventions through the use of technological innovations can address this need while reducing costs.
About 10 million people need long term care in the US2. Of these, about 4.6 million are older than 65 and live in the community. These 4.5 million represent a potential $89.1 billion in cost savings if everyone had access and participated in the RN nurse care coordinator intervention that has been tested at MU3-7. This is more than 40% of all dollars spent on people with long-term health needs in the US. Technology coupled with nurse care coordination has huge potential to help older people stay at home, where they want to be, safely and more cost-effectively.
In-Home Sensor Networks for Detection of Early illness and Functional Decline
The Aging in Place research and practice team, initiated in 1996, has successfully developed and tested the aging in place model of care and conducted research on the cost and clinical outcomes. The Eldertech research team, initiated in 2003 and now led by the Center for Eldercare and Rehabilitation Technology in the College of Engineering, is successfully engaged with key researchers from the Schools of Nursing, Medicine, Social Work, Health Management and Informatics, Health Professions, and others at MU. The Eldertech team is nationally and internationally recognized for their cutting edge interdisciplinary research on technological solutions for the complex problems facing elders as they want to age in place.
Sensor networks have been installed in TigerPlace apartments since Fall, 2005. The suite of sensors includes motion sensors, chair pads, a stove sensor, and a bed sensor capturing restlessness, and low, normal, and high pulse and respiration rates. We have developed an integrated intelligent monitoring system that reliably captures data about the residents and their environment in a noninvasive manner and balances the needs of health and safety and privacy, developed algorithms to extract patterns of activity from the collected sensor data and generate alerts that indicate a potential health change, evaluated the usability of the interfaces, and investigated the acceptability of the technology by seniors. Figures 3-4 show examples of sensor data displays and illustrate changes in patterns that follow health changes.
In a recent NIH study, we showed statistically significant differences in health outcomes between a control group and an intervention group in which early illness alerts (based on sensor data) were automatically sent to nurses11. Nurses rated the clinical relevance of the alerts and their potential in aiding early interventions; this information has been captured in a database for future development of early illness alert algorithms.
Passive Fall Detection and Gait Analysis for Fall Risk Assessment
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. Our approach to fall detection does not require the client to wear anything, push any buttons, or charge any batteries. Rather, we have been investigating sensing that can be embedded in the environment, including vision, depth images (e.g., the Kinect), acoustic arrays, and radar12-14. Likewise, fall risk assessment is accomplished through daily monitoring in the home, also using sensing installed in the environment 15-18, to capture gait changes that may indicate problems in physical or cognitive health. Figure 5 shows a 3D voxel model constructed from two webcam images; silhouettes are used to anonymize the images for privacy protection. Footsteps are extracted from the voxel model for the measurement of gait parameters.
Sensing systems are being rigorously studied in the lab with a motion capture system for validation. Stunt actors are trained to fall in 21 different falls typical of older adults and then act out the falls for data collection. Volunteers aged 20 to 90 have participated in studies that have validated the measurement of fall risk parameters such as walking speed, step time, step length, sit to stand time, and body sway. We are also testing this approach in TigerPlace apartments with the webcam voxel system, the Kinect, and the radar system. Gait parameters are being captured automatically as residents walk around the home in their normal, daily activities. These systems have been installed in ten TigerPlace apartments and will remain in place for two years; data are collected monthly with the residents on fall risk instruments and with stunt actors for falls to provide ground truth in the home.
Physiological Monitoring with a Passive Hydraulic Bed Sensor
The MU eldertech team has developed a new hydraulic bed sensor that captures quantitative pulse and respiration rates as well as bed restlessness19. Figure 6 shows the prototype with data collected while positioned under the bed mattress. Algorithms automatically separate the balistocardiogram heart signal from the respiration signal to compute pulse and respiration rates. This system will become part of the early illness alert system. The hydraulic bed sensor provides more finely grained information for detecting changes in sleep patterns and physiological signals that may indicate changing health conditions.
Fig. 6. The hydraulic bed sensor prototype with 10 seconds of data