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Life-Logging of Wheelchair Driving on Web Maps for Visualizing Potential Accidents and Incidents

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7458)

Abstract

Life-logging has attracted rising attention as the most fundamental elements for developing every rich software today. This paper presents computational estimation and mapping of potential accidents and incidents of wheelchairs from life-logs with a single cheap and mini-sized three-axis accelerometer mounted on a wheelchair. Wheelchair driving data was obtained by real wheelchair users driving with their wheelchair on real roads, but has the sampling time delay and noises. As a first step of computational estimation, wheelchair driving behavior was classified into moving and static action, and the moving action was divided into tough and smooth status of the ground surface. We employed Support Vector Machine for classification, and made the precise supervised data from the video of wheelchair driving. As the result of classification, estimation of moving/static was achieved 98.2% accuracy rate and estimation of tough/smooth surface was achieved 82.6% accuracy rate. From the surface estimation result, wheelchair-driving difficulty was mapped and evaluated.

Keywords

  • Life-Log
  • SVM
  • time-series classification
  • wheelchair

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  • DOI: 10.1007/978-3-642-32695-0_16
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Iwasawa, Y., Yairi, I.E. (2012). Life-Logging of Wheelchair Driving on Web Maps for Visualizing Potential Accidents and Incidents. In: Anthony, P., Ishizuka, M., Lukose, D. (eds) PRICAI 2012: Trends in Artificial Intelligence. PRICAI 2012. Lecture Notes in Computer Science(), vol 7458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32695-0_16

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  • DOI: https://doi.org/10.1007/978-3-642-32695-0_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32694-3

  • Online ISBN: 978-3-642-32695-0

  • eBook Packages: Computer ScienceComputer Science (R0)