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Multi-view Onboard Clustering of Skeleton Data for Fall Risk Discovery

  • Daisuke Takayama
  • Yutaka Deguchi
  • Shigeru Takano
  • Vasile-Marian Scuturici
  • Jean-Marc Petit
  • Einoshin SuzukiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8850)

Abstract

We propose a multi-view onboard clustering of skeleton data for fall risk discovery. Clustering by an autonomous mobile robot opens the possibility for monitoring older adults from the most appropriate positions, respecting their privacies, and adapting to various changes. Since the data that the robot observes is a data stream and communication network can be unreliable, the clustering method in this case should be onboard. Motivated by the rapid increase of older adults in number and the severe outcomes of their falls, we adopt Kinect equipped robots and focus on gait skeleton analysis for fall risk discovery. Our key contributions are new between-skeleton distance measures for risk discovery and two series of experiments with our onboard clustering. The experiments revealed several key findings for the method and the application as well as interesting outcomes such as clusters which consist of unexpected risky postures.

Keywords

Skeleton Clustering Service-oriented DBMS Human Monitoring Mobile Robots 

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Notes

Acknowledgments

A part of this research was supported by a Bilateral Joint Research Project between Japan and France funded by JSPS and CNRS (CNRS/JSPS PRC 0672), and JSPS KAKENHI 24650070 and 25280085.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Daisuke Takayama
    • 1
  • Yutaka Deguchi
    • 1
  • Shigeru Takano
    • 1
  • Vasile-Marian Scuturici
    • 2
  • Jean-Marc Petit
    • 2
  • Einoshin Suzuki
    • 1
    Email author
  1. 1.Dept. Informatics, ISEEKyushu UniversityFukuokaJapan
  2. 2.LIRISUniversité de Lyon, CNRS, INSALyonFrance

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