Fall Detection on Embedded Platform Using Kinect and Wireless Accelerometer

  • Michal Kepski
  • Bogdan Kwolek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7383)


In this paper we demonstrate how to accomplish reliable fall detection on a low-cost embedded platform. The detection is achieved by a fuzzy inference system using Kinect and a wearable motion-sensing device that consists of accelerometer and gyroscope. The foreground objects are detected using depth images obtained by Kinect, which is able to extract such images in a room that is dark to our eyes. The system has been implemented on the PandaBoard ES and runs in real-time. It permits unobtrusive fall detection as well as preserves privacy of the user. The experimental results indicate high effectiveness of fall detection.


Fuzzy Inference System Depth Image Assistive Technology Foreground Object Wearable Device 
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

  • Michal Kepski
    • 1
  • Bogdan Kwolek
    • 1
  1. 1.Rzeszów University of TechnologyRzeszówPoland

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