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

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ICT Innovations 2015 (ICT Innovations 2015)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 399))

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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.

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Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, Universita’ Politecnica delle Marche, Ancona, Italy, I-60131

    Samuele Gasparrini, Enea Cippitelli, Ennio Gambi & Susanna Spinsante

  2. School of Technology and Health, KTH, Royal Institute of Technology, Stockholm, Sweden, SE-100 44

    Jonas Wåhslén, Ibrahim Orhan & Thomas Lindh

Authors
  1. Samuele Gasparrini
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  2. Enea Cippitelli
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  3. Ennio Gambi
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  4. Susanna Spinsante
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  5. Jonas Wåhslén
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  6. Ibrahim Orhan
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  7. Thomas Lindh
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Corresponding author

Correspondence to Samuele Gasparrini .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Engineering, Ss. Cyril and Methodious University, Skopje, North Macedonia

    Suzana Loshkovska

  2. Faculty of Computer Science, University Goce Delcev, Stip, North Macedonia

    Saso Koceski

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© 2016 Springer International Publishing Switzerland

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Cite this paper

Gasparrini, S. et al. (2016). Proposal and Experimental Evaluation of Fall Detection Solution Based on Wearable and Depth Data Fusion. In: Loshkovska, S., Koceski, S. (eds) ICT Innovations 2015 . ICT Innovations 2015. Advances in Intelligent Systems and Computing, vol 399. Springer, Cham. https://doi.org/10.1007/978-3-319-25733-4_11

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  • DOI: https://doi.org/10.1007/978-3-319-25733-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25731-0

  • Online ISBN: 978-3-319-25733-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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