TigerPlace

Early detection of health changes among older adults is the key to maintaining health, independence, and function. Non-wearable sensors such as depth cameras, motion sensors (passive infrared, PIR) and bed sensors (based on ballistocardiography) are able to detect changes...

The goal of this study was to compare utilization and cost outcomes of patients who received long-term care coordination in an Aging in Place program to patients who received care coordination as a routine service in home health care. This...

Older adults prefer to age in place, remaining in their home as their health care needs intensify. In a state evaluation of Aging in Place (AIP), the University of Missouri Sinclair School of Nursing and Americare System Inc., developed...

Objectives: Measure the clinical effectiveness and cost-effectiveness of using sensor data from an environmentally embedded sensor system for early illness recognition. This sensor system has demonstrated in pilot studies to detect changes in function and in chronic diseases or...

In this paper, we propose a Pulse-Doppler radar system for in-home gait assessment of older adults. A methodology has been developed to extract gait parameters including walking speed and step time using Doppler radar. The gait parameters have been...

When planning the Aging in Place Initiative at TigerPlace, it was envisioned that advances in technology research had the potential to enable early intervention in health changes that could assist in proactive management of health for older adults and...

Falls are a major problem in older adults. A continuous, unobtrusive, environmentally mounted (i.e., embedded into the environment and not worn by the individual), in home monitoring system that automatically detects when falls have occurred or when the risk...

One in three elders over the age of 65 falls each year in the United States. This paper describes a non-invasive fall detection system based on a Doppler radar sensor. The developed system has been tested in two environments:...

This study explored using Big Data, totaling 66 terabytes over 10 years, captured from sensor systems installed in independent living apartments to predict falls from pre-fall changes in residents’ Kinect-recorded gait parameters. Over a period of 3 to 48...

We propose a non-wearable hydraulic bed sensor system that is placed underneath the mattress to estimate the relative systolic blood pressure of a subject, which only differs from the actual blood pressure by a scaling and an offset factor....