A pattern analysis and visualisation system for sleep monitoring in ambient assisted living environment

  • Authors:

  • Huiru Zheng;Haiying Wang;Hoda Nikamalfard;Maurice Mulvenna;Paul McCullagh;Suzanne Martin;Jonathan G. Wallace;Paul Jeffers

  • Affiliations:

  • School of Computing and Mathematics, University of Ulster at Jordanstown Co. Antrim, BT37 0QB, Northern Ireland, UK;School of Computing and Mathematics, University of Ulster at Jordanstown Co. Antrim, BT37 0QB, Northern Ireland, UK;School of Computing and Mathematics, University of Ulster at Jordanstown Co. Antrim, BT37 0QB, Northern Ireland, UK;School of Computing and Mathematics, University of Ulster at Jordanstown Co. Antrim, BT37 0QB, Northern Ireland, UK;School of Computing and Mathematics, University of Ulster at Jordanstown Co. Antrim, BT37 0QB, Northern Ireland, UK;School of Computing and Mathematics, University of Ulster at Jordanstown Co. Antrim, BT37 0QB, Northern Ireland, UK;School of Computing and Mathematics, University of Ulster at Jordanstown Co. Antrim, BT37 0QB, Northern Ireland, UK;Fold Housing Association, 3-7 Redburn Square, Holywood BT18 9HZ, Northern Ireland, UK

  • Venue:
  • International Journal of Computers in Healthcare
  • Year:
  • 2012

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Abstract

Sleep disturbances are among the most distressing of all Alzheimer's disease-related symptoms, and may be a marker for early Alzheimer's disease in some cases. Assisted technologies have been applied in telecare services to provide care to people either in their own homes or in supported housing, via monitoring their activities, including sleep. In this paper, we present a sleep pattern detection and visualisation system, PAViS, developed to support the tele-monitoring and assessment of sleep disturbances for people diagnosed with early dementia. The system provides visual tool kits for telecare service providers and investigators to view sleep profiles and analyse sleep patterns based on sensory data gathered at users' home.