Fuzzy Decision Making Model for Human Fall Detection and Inactivity Monitoring

  • Marina V. Sokolova
  • Antonio Fernández-Caballero
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 15)


In this paper a fuzzy decision making model for fall detection and inactivity monitoring is presented. The inputs to the model are regions of interest (ROIs) corresponding to humans segmented in infrared video. The classification features proposed in fuzzy decision include geometric and kinematic parameters associated with more or less sudden changes in the tracked bounding boxes of the ROIs. The paper introduces a complete fuzzy-based fall detection system capable of identifying true and false falls, enhanced with inactivity monitoring aimed at confirming the need for medical assistance and/or care. The fall indicators used as well as their fuzzy model is explained in detail. As the proposed model has been tested for a wide number of static and dynamic falls, some exciting initial results are presented here.


Fuzzy Model Linguistic Variable Fuzzy Decision Fall Detection Pedestrian 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-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marina V. Sokolova
    • 1
    • 2
  • Antonio Fernández-Caballero
    • 3
  1. 1.Instituto de Investigación en Informática de Albacete (I3A)Universidad de Castilla-La ManchaAlbaceteSpain
  2. 2.South-West State UniversityKurskRussia
  3. 3.Departamento de Sistemas InformáticosUniversidad de Castilla-La ManchaAlbaceteSpain

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