A fuzzy model for human fall detection in infrared video

  • Authors:

  • Marina V. Sokolova;Juan Serrano-Cuerda;José Carlos Castillo;Antonio Fernández-Caballero

  • Affiliations:

  • South-West State University, Kursk, Russia;Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, Spain;Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, Spain;Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, Spain

  • Venue:
  • Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
  • Year:
  • 2013

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Abstract

Fall detection, especially for elderly people, is a challenging problem which demands new products and technologies. In this paper a fuzzy model for fall detection and inactivity monitoring in infrared video is presented. The classification features proposed include geometric and kinematic parameters associated with more or less sudden changes in the tracked human-related regions of interest. A complete segmentation and tracking algorithm for infrared video as well as a fuzzy fall detection and confirmation algorithm are introduced. The proposed system is 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. The fuzzy model has been tested for a wide number of static and dynamic falls, demonstrating exciting initial results.