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NONCADO: A System to Prevent Falls by Encouraging Healthy Habits in Elderly People

  • Elisa SalviEmail author
  • Silvia Panzarasa
  • Riccardo Bagarotti
  • Michela Picardi
  • Rosangela Boninsegna
  • Irma Sterpi
  • Massimo Corbo
  • Giordano Lanzola
  • Silvana Quaglini
  • Lucia Sacchi
Conference paper
  • 762 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11526)

Abstract

Falls in the elderly are a known problem, leading to hospitalization, impaired life quality, and social costs. Falls are associated to multiple risk factors, related to the subject’s health, lifestyle, and living environment. Living alone makes it difficult to detect a patient’s decline, which increases the fall risk. In this paper, we present NONCADO, a project funded by the Lombardy Region (Italy), aimed at developing a system for preventing falls in the elderly living alone, by integrating data from a network of sensors (both wearable and environmental). The collected data are analyzed by a decision support system (DSS) that exploits advanced temporal data analysis techniques to detect behaviors known to increase the individual risk (e.g. moving within the house with inadequate lighting, or performing not enough physical activity). A daily report listing the detected risky behaviors is produced and delivered through a mobile app. Since we address long-term monitoring, it’s important to detect as well the changes in a subject’s habits that may increase fall risk. Such changes are summarized in a weekly report. A preliminary feasibility evaluation of the system was performed during a 2-weeks pilot study involving 16 patients treated at the Casa di Cura Privata del Policlinico hospital, in Milan, Italy. Patients were asked to perform 5 activities, and the system’s ability to correctly detect them was assessed. The study results were encouraging, as the system reached an overall accuracy of 90%.

Keywords

Fall risk Temporal data analysis Home monitoring 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Elisa Salvi
    • 1
    Email author
  • Silvia Panzarasa
    • 1
  • Riccardo Bagarotti
    • 1
  • Michela Picardi
    • 2
  • Rosangela Boninsegna
    • 2
  • Irma Sterpi
    • 2
  • Massimo Corbo
    • 2
  • Giordano Lanzola
    • 1
  • Silvana Quaglini
    • 1
  • Lucia Sacchi
    • 1
  1. 1.University of PaviaPaviaItaly
  2. 2.Department of Neurorehabilitation SciencesCasa di Cura Privata del Policlinico (CCP)MilanItaly

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