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Fall Detection Algorithm Based on Thresholds and Residual Events

  • Fily M. Grisales-FrancoEmail author
  • Francisco Vargas
  • Álvaro Ángel Orozco
  • Mauricio A. Alvarez
  • German Castellanos-Dominguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9423)

Abstract

Falling is a risk factor of vital importance in elderly adults, hence, the ability to detect falls automatically is necessary to minimize the risk of injury. In this work, we develop a fall detection algorithm based in inertial sensors due its scope of activity, portability, and low cost. This algorithm detects the fall across thresholds and residual events after that occurs, for this it filters the acceleration data through three filtering methodologies and by means of the amount of acceleration difference falls from Activities of Daily Living (ADLs). The algorithm is tested in a human activity and fall dataset, showing improves respect to performance compared with algorithms detailed in the literature.

Keywords

Residual Event Inertial Sensor Acceleration Data Wearable Device Fall Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Fily M. Grisales-Franco
    • 1
    Email author
  • Francisco Vargas
    • 2
  • Álvaro Ángel Orozco
    • 3
  • Mauricio A. Alvarez
    • 3
  • German Castellanos-Dominguez
    • 1
  1. 1.Signal Processing and Recognition GroupUniversidad Nacional de ColombiaManizalesColombia
  2. 2.SISTEMIC, Faculty of EngineeringUniversidad de Antioquia UdeAMedellínColombia
  3. 3.Grupo de Investigacion En AutomaticaUniversidad Tecnologica de PereiraPereiraColombia

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