The Wordometer 2.0 - Estimating the number of words you read in real life using commercial EOG Glasses

Shoya Ishimaru, Koichi Kise, Kai Steven Kunze, Andreas Dengel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

On the basis of the motivation to increase daily reading volumes, this paper introduces an implementation of "Wordometer 2.0," which counts the number of read words in a day. While word count estimation using eye tracking glasses or medical EOG (Electrooculography) sensors has been already proposed, our aim is to implement the idea on an affordable device to quantify readings in the whole day. We utilize commercial EOG glasses designed for everyday use. Our method was evaluated with a dataset involving five readers with 11 % error rate with user-independent training.

Original languageEnglish
Title of host publicationUbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PublisherAssociation for Computing Machinery, Inc
Pages293-296
Number of pages4
ISBN (Electronic)9781450344623
DOIs
Publication statusPublished - 2016 Sep 12
Event2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 - Heidelberg, Germany
Duration: 2016 Sep 122016 Sep 16

Other

Other2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016
CountryGermany
CityHeidelberg
Period16/9/1216/9/16

Keywords

  • Electrooculography
  • Eye movement
  • Quantified self
  • Reading

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software
  • Information Systems
  • Computer Networks and Communications
  • Human-Computer Interaction

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  • Cite this

    Ishimaru, S., Kise, K., Kunze, K. S., & Dengel, A. (2016). The Wordometer 2.0 - Estimating the number of words you read in real life using commercial EOG Glasses. In UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 293-296). Association for Computing Machinery, Inc. https://doi.org/10.1145/2968219.2971398