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.
TigerPlace (named after the University of Missouri mascot, the tiger) is a specially designed elder housing project that was initiated by the MU Sinclair School of Nursing (SON) and designed by MU faculty working with the Americare Corporation of Sikeston, Missouri.
TigerPlace is built to nursing home standards, but not the typical configuration. The building has 32 private apartments with fully accessible bathrooms, kitchens, and screened porches. Private garages and a private dining room for special family occasions are available, as are beautiful common spaces such as a large living room, dining room, meeting room, library, sports bar, and beauty shop. Included in this list of amenities that surpasses the typical list of long term care options are TigerPlace Pet Initiative (TiPPI) Veterinary Medicine Clinic, TigerCare Wellness Center, and TigerCize Exercise and Spa area.
A major goal for MU is to design and implement exciting research, education, and practice opportunities at TigerPlace while integrating TigerPlace into the MU campus and the Columbia community. From the resident’s point of view, on-going assessment, early illness recognition, health promotion activities, and a well-designed housing environment will help older people stay healthier and active longer, avoid expensive and debilitating hospitalizations, and for most residents, avoid relocation to a nursing home. The links with MU are important as seniors become involved in the student learning projects and take advantage of classes and cultural activities of their interest at MU.
In the area below you will find a list of recently added TigerPlace research articles.
Our team has developed a technological innovation that detects changes in health
status that indicate impending acute illness or exacerbation of chronic illness before usual assessment methods or self-reports of illness. We successfully used this information in a 1-year prospective study to alert health care providers so they could readily assess the situation and initiate early treatment to improve functional independence. Intervention participants showed significant improvements (as compared with the control group) for the Short Physical Performance Battery gait speed score at Quarter 3 (p = 0.03), hand grip-left at Quarter 2 (p = 0.02), hand gripright at Quarter 4 (p = 0.05), and the GAITRite functional ambulation profile score at Quarter 2 (p = 0.05). Technological methods such as these could be widely adopted in older adult housing, long-term care settings, and in private homes where older adults wish to remain independent for as long as possible.
Falling is a common health problem for elders. It is reported that more than one third of seniors 65 and older fall each year in the United States. We develop a dual Doppler radar system for fall detection. The radar system generates a specific Doppler signature for each human activity which is then categorized by a set of classifiers as fall or non-fall. However, different classifiers may produce different decisions for the same signature. In this paper, we propose a fusion methodology based on the Choquet integral that combines partial decision information from each sensor and each classifier to form a final fall/non-fall decision. We employ Melfrequency cepstral coefficients (MFCC) to represent the
Doppler signatures of various human activities such as walking, bending down, and falling. Then we use three different classifiers, kNN, SVM and Bayes, to detect falls based on the extracted MFCC features. Each partial decision from a
classifier is represented as a confidence. We apply our fusion method to a dataset that consists in 450 activity samples (109 falls and 341 non-falls).
Purpose: This study investigates whether motion density maps based on passive infrared (PIR) motion sensors and the average time out and average density per hour measures of the density map are sensitive enough to detect changes in mental health over time. Method: Within the sensor network, data are logged from PIR motion sensors which capture motion events as people move around the home. If there is continuous motion, the sensor will generate events at 7 second intervals. If the resident is less active, events will be generated less frequently. A web application displays the data as activity density maps showing events per hour with hours on the vertical axis and progressive days on the horizontal axis. Color and intensity provide textural indications of time spent away from home and activity level. Texture features from the co-occurrence matrix are used to capture the periodicity pattern of the activity (including homogeneity, local variation, and entropy) and are combined with the average motion density per hour and the average time away from home. The similarity of two different density maps is represented by a number that is computed in feature space as the distance from one map to the other, or a measure of dis-similarity. Employing a retrospective approach, density maps were compared with health assessment information (Geriatric Depression Scale, Mini Mental State Exam, and Short Form Health Survey -12) to determine congruence between activity pattern changes and the health information20. A case by case study method, analyzed the density maps of 5 individuals with identified mental health issues. These density maps were reviewed along with the averages of time out of apartment per day per hour and average density per hour for hours at home and mental health assessment scores to determine if there were activity changes and if activity patterns reflected changes in mental health conditions. Results & Discussion: The motion density maps show visual changes in the client’s activity, including circadian rhythm, time away from home, and general activity level (sedentary vs. puttering). The measures are sensitive enough, yielding averages of time out of apartment and average density per hour for hours at home that indicate significant change. There is evidence of congruence with health assessment scores. This pilot study demonstrates that density maps can be used as a tool for early illness detection. The results indicate that sensor technology has the potential to augment traditional health care assessments and care coordination.
Key words: passive sensors, early illness detection, technology and mental illness, motion density mapping
Falls are a major cause of injury in the elderly with almost 1/3rd of people aged 65 and more falling each year. This work aims to use gait measurements from everyday living environments to estimate risk of falling and enable improved interventions. For this purpose, we consider the use of low-cost pulse-Doppler range control radar. These radars can continuously acquire data during normal activity of a person in night and day conditions and even in the presence of obstructing furniture. A short-time Fourier transform of the radar data reveals unique Doppler signatures from the torso motion and the leg swings. Two algorithms that can extract these features from the radar spectrogram are proposed in this study for estimating gait velocity and stride durations. The performance of the proposed radar system is evaluated with an extensive set of experimental data, which consists of 9 different walk types and a total of 27 separate tests. A high accuracy motion-capture camera system has also been used to acquire data simultaneously with the radar and provides the ground truth reference. Results indicate that the proposed radar system is a viable candidate for gait characterization and can be used to accurately track mean gait velocity, mean stride duration and stride duration variability. The gait velocity variability can also be estimated but with relatively larger error levels.
Integrated sensor networks have been installed in apartments of volunteer residents at TigerPlace, an aging in place retirement community that allows residents to remain in their apartments even if their health deteriorates. The sensor networks supplement registered nurse (RN) care coordination provided by Sinclair Home Care by alerting the RN care coordinator about changes in the normal sensor patterns. In several cases, the alerts have prompted the care coordinator to have the resident tested for urinary tract infections. Importantly, the sensor network detected signs of illness earlier than traditional health care assessment.
Keywords- Aging in Place, Sensor Network, Illness Detection
Many older adults in the US prefer to live independently for as long as they are able, despite the onset of conditions such as frailty and dementia. Sensor networks have emerged in the last decade, together with telehealth and internet based electronic health records (EHR), as a possible solution to older adult health monitoring. Many commercial solutions for EHRs, telehealth monitoring and sensor networks are available but, as far as we know, no integrated system exists. In this paper we present an integrated eldercare EHR system (IEEHR) that merges health data with sensor and telehealth (vital signs) measurements. The benefit of an EEHR system is three fold: provides physicians a wider gamut of tools for chronic disease management, reduces nursing workload and allows the development of health context aware algorithms for predictive health assessment. In this paper we present the integrated EEHR system we are developing at TigerPlace, an assisted living community in Columbia, Missouri. Several examples of possible applications are also presented.
Index Terms—Eldercare, Electronic Health Records,Sensor Networks, Telemedicine, Predictive Health Assessment