A smart home application to eldercare: current status and lessons learned.
To address an aging population, we have been investigating sensor networks for monitoring older adults in their homes. In this paper, we report ongoing work in which passive sensor networks have been installed in 17 apartments in an aging in place eldercare facility. The network under development includes simple motion sensors, video sensors, and a bed sensor that captures sleep restlessness and pulse and respiration levels. Data collection has been ongoing for over two years in some apartments. This longevity in sensor data collection is allowing us to study the data and develop algorithms for identifying alert conditions such as falls, as well as extracting typical daily activity patterns for an individual. The goal is to capture patterns representing physical and cognitive health conditions and then recognize when activity patterns begin to deviate from the norm. In doing so, we strive to provide early detection of potential problems which may lead to serious health events if left unattended. We describe the components of the network and show examples of logged sensor data with correlated references to health events. A summary is also included on the challenges encountered and the lessons learned as a result of our experiences in monitoring aging adults in their homes.
Skubic
Marjorie
M
Electrical and Computer Engineering Department, University of Missouri, Columbia, MO, USA. skubicm@missouri.edu
Alexander
Gregory
G
Popescu
Mihail
M
Rantz
Marilyn
M
Keller
James
J
D000203:Activities of Daily Living; D000368:Aged; D001185:Artificial Intelligence; D004779:Environment Design; D006299:Health Services for the Aged; D006699:Home Care Services; D016852:Housing for the Elderly; D006801:Humans; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D013686:Telemetry / Q000295:instrumentation / Q000379:methods