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Unobtrusive Technological Approach for Continuous Behavior Change Detection Toward Better Adaptation of Clinical Assessments and Interventions for Elderly People

  • Firas Kaddachi
  • Hamdi Aloulou
  • Bessam Abdulrazak
  • Philippe Fraisse
  • Mounir Mokhtari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10461)

Abstract

Behavior change indicates continuous decline in physical, cognitive and emotional status of elderly people. Early detection of behavior change is major enabler for service providers to adapt their services and improve the quality of life of elderly people. Nowadays, existing psychogeriatric scales and questionnaires are insufficient to observe all possible changes at a daily basis. Therefore, we propose a technological approach for behavior change detection, that employs unobtrusive ambient technologies to follow up elderly people over long periods. In fact, we study significant behavior change indicators (e.g., sleep impairments, visits and go out) and investigate statistical techniques that distinguish transient and continuous changes in monitored behavior. Furthermore, we present a preliminary validation of our approach through results based on correlations between our technological observations and medical observations of two-year nursing home deployment.

Keywords

Behavior change detection Elderly people Unobtrusive technologies Statistical analysis techniques 

Notes

Acknowledgement

We give our special thanks to Saint Vincent de Paul nursing home in Occagnes, France. Our deployment in this nursing home is also supported by VHP inter@ctive project and the Quality Of Life chair.

Our work is part of the European project City4Age that received funding from the Horizon 2020 research and innovation program under grant agreement number 689731.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Firas Kaddachi
    • 1
  • Hamdi Aloulou
    • 1
  • Bessam Abdulrazak
    • 2
  • Philippe Fraisse
    • 1
  • Mounir Mokhtari
    • 3
  1. 1.Montpellier Laboratory of InformaticsRobotics and Microelectronics (LIRMM)MontpellierFrance
  2. 2.University of SherbrookeQuebecCanada
  3. 3.Institut Mines-Telecom (IMT)ParisFrance

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