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Proposal and Experimental Evaluation of Fall Detection Solution Based on Wearable and Depth Data Fusion

  • Samuele Gasparrini
  • Enea Cippitelli
  • Ennio Gambi
  • Susanna Spinsante
  • Jonas Wåhslén
  • Ibrahim Orhan
  • Thomas Lindh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 399)

Abstract

Fall injury issues represent a serious problem for elderly in our society. These people want to live in their home as long as possible and technology can improve their security and independence. In this work we study the joint use of a camera based system and wearable devices, in the so called data fusion approach, to design a fall detection solution. The synchronization issues between the heterogeneous data provided by the devices are properly treated, and three different fall detection algorithms are implemented. Experimental results are also provided, to compare the proposed solutions.

Keywords

Depth camera Inertial sensor Data fusion Synchronization Fall detection 

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Copyright information

© Springer International Publishing Switzerland 2016

<SimplePara><Emphasis Type="Bold">Open Access</Emphasis> This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. </SimplePara> <SimplePara>The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.</SimplePara>

Authors and Affiliations

  • Samuele Gasparrini
    • 1
  • Enea Cippitelli
    • 1
  • Ennio Gambi
    • 1
  • Susanna Spinsante
    • 1
  • Jonas Wåhslén
    • 2
  • Ibrahim Orhan
    • 2
  • Thomas Lindh
    • 2
  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversita’ Politecnica delle MarcheAnconaItaly
  2. 2.School of Technology and HealthKTH, Royal Institute of TechnologyStockholmSweden

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