A smart home application to eldercare: Current status and lessons learned

  • Authors:

  • Marjorie Skubic;Gregory Alexander;Mihail Popescu;Marilyn Rantz;James Keller

  • Affiliations:

  • (Correspd. E-mail: skubicm@missouri.edu) Electrical and Computer Engineering Department, Univ. of Missouri, Columbia, MO, USA and Center for Eldercare and Rehabilitation Technology, Univ. of Misso ...;Sinclair School of Nursing, University of Missouri, Columbia, MO, USA and Center for Eldercare and Rehabilitation Technology, University of Missouri, Columbia, MO, USA;Dept. of Health Management and Informatics, Univ. of Missouri, Columbia, MO, USA and Informatics Institute, Univ. of Missouri, Columbia, MO, USA and Center for Eldercare and Rehabilitation Technol ...;Sinclair School of Nursing, Univ. of Missouri, Columbia, MO, USA and Center for Eldercare and Rehabilitation Technology, Univ. of Missouri, Columbia, MO, USA and Center for Eldercare and Rehabilit ...;Electrical and Computer Engineering Department, Univ. of Missouri, Columbia, MO, USA and Center for Eldercare and Rehabilitation Technology, Univ. of Missouri, Columbia, MO, USA

  • Venue:
  • Technology and Health Care - Smart Environments: Technology to Support Healthcare
  • Year:
  • 2009

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Abstract

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.