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
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.
In this randomized controlled study, we are investigating and refining health alerts produced by environmentally-embedded in-home sensor networks designed to detect early signs of health change and functional decline in older adults, the keys to successful intervention.
The main goal of this work was to develop algorithms for early illness recognition in elderly. Early illness recognition (EIR) is important, as research has shown that results in better medical outcomes and a reduction in health care cost. We developed methodologies (see Figure 1) that link sensor data to the medical (nursing) records for monitoring the residents of TigerPlace, an aging in place community from Columbia, Missouri.
The dream of older Americans is to remain as active and independent as possible for as long as possible. They want to age in place, not in institutions like nursing homes. Recently, enabling technology in the form of low cost sensors, computers, and communications systems has become available, which with supportive health care services makes the dream of aging in place a reality.