Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography

  • Andreas Bulling
  • Jamie A. Ward
  • Hans Gellersen
  • Gerhard Tröster
Conference paper

DOI: 10.1007/978-3-540-79576-6_2

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5013)
Cite this paper as:
Bulling A., Ward J.A., Gellersen H., Tröster G. (2008) Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography. In: Indulska J., Patterson D.J., Rodden T., Ott M. (eds) Pervasive Computing. Pervasive 2008. Lecture Notes in Computer Science, vol 5013. Springer, Berlin, Heidelberg

Abstract

In this work we analyse the eye movements of people in transit in an everyday environment using a wearable electrooculographic (EOG) system. We compare three approaches for continuous recognition of reading activities: a string matching algorithm which exploits typical characteristics of reading signals, such as saccades and fixations; and two variants of Hidden Markov Models (HMMs) - mixed Gaussian and discrete. The recognition algorithms are evaluated in an experiment performed with eight subjects reading freely chosen text without pictures while sitting at a desk, standing, walking indoors and outdoors, and riding a tram. A total dataset of roughly 6 hours was collected with reading activity accounting for about half of the time. We were able to detect reading activities over all subjects with a top recognition rate of 80.2% (71.0% recall, 11.6% false positives) using string matching. We show that EOG is a potentially robust technique for reading recognition across a number of typical daily situations.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Andreas Bulling
    • 1
  • Jamie A. Ward
    • 2
  • Hans Gellersen
    • 2
  • Gerhard Tröster
    • 1
  1. 1.Wearable Computing LaboratoryETH ZurichSwitzerland
  2. 2.Embedded Interactive Systems GroupLancaster UniversityUK

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