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Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection

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  • Eduardo Casilari
  • Jose Antonio Santoyo-Ramón
  • Jose Manuel Cano-García

Abstract

During the last years, many research efforts have been devoted to the definition of Fall Detection Systems (FDSs) that benefit from the inherent computing, communication and sensing capabilities of smartphones. However, employing a smartphone as the unique sensor in a FDS application entails several disadvantages as long as an accurate characterization of the patient’s mobility may force to transport this personal device on an unnatural position. This paper presents a smartphone-based architecture for the automatic detection of falls. The system incorporates a set of small sensing motes that can communicate with the smartphone to help in the fall detection decision. The deployed architecture is systematically evaluated in a testbed with experimental users in order to determine the number and positions of the sensors that optimize the effectiveness of the FDS, as well as to assess the most convenient role of the smartphone in the architecture.

Suggested Citation

  • Eduardo Casilari & Jose Antonio Santoyo-Ramón & Jose Manuel Cano-García, 2016. "Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-17, December.
    • Handle: RePEc:plo:pone00:0168069
    DOI: 10.1371/journal.pone.0168069
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    References listed on IDEAS

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    1. Mark V Albert & Konrad Kording & Megan Herrmann & Arun Jayaraman, 2012. "Fall Classification by Machine Learning Using Mobile Phones," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-6, May.
    2. Carlos Medrano & Raul Igual & Inmaculada Plaza & Manuel Castro, 2014. "Detecting Falls as Novelties in Acceleration Patterns Acquired with Smartphones," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-9, April.
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