I know what you are reading - recognition of document types using mobile eye tracking

Kai Steven Kunze, Yuzuko Utsumi, Yuki Shiga, Koichi Kise, Andreas Bulling

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

38 Citations (Scopus)

Abstract

Reading is a ubiquitous activity that many people even perform in transit, such as while on the bus or while walking. Tracking reading enables us to gain more insights about expertise level and potential knowledge of users - towards a reading log tracking and improve knowledge acquisition. As a first step towards this vision, in this work we investigate whether different document types can be automatically detected from visual behaviour recorded using a mobile eye tracker. We present an initial recognition approach that combines special purpose eye movement features as well as machine learning for document type detection. We evaluate our approach in a user study with eight participants and five Japanese document types and achieve a recognition performance of 74% using user-independent training.

Original languageEnglish
Title of host publicationISWC 2013 - Proceedings of the 2013 ACM International Symposium on Wearable Computers
Pages113-116
Number of pages4
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 17th ACM International Symposium on Wearable Computers, ISWC 2013 - Zurich, Switzerland
Duration: 2013 Sep 92013 Sep 12

Other

Other2013 17th ACM International Symposium on Wearable Computers, ISWC 2013
CountrySwitzerland
CityZurich
Period13/9/913/9/12

Fingerprint

Eye movements
Knowledge acquisition
Learning systems

Keywords

  • Document classification
  • Eye tracking
  • Reading behavior

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Software

Cite this

Kunze, K. S., Utsumi, Y., Shiga, Y., Kise, K., & Bulling, A. (2013). I know what you are reading - recognition of document types using mobile eye tracking. In ISWC 2013 - Proceedings of the 2013 ACM International Symposium on Wearable Computers (pp. 113-116) https://doi.org/10.1145/2493988.2494354

I know what you are reading - recognition of document types using mobile eye tracking. / Kunze, Kai Steven; Utsumi, Yuzuko; Shiga, Yuki; Kise, Koichi; Bulling, Andreas.

ISWC 2013 - Proceedings of the 2013 ACM International Symposium on Wearable Computers. 2013. p. 113-116.

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

Kunze, KS, Utsumi, Y, Shiga, Y, Kise, K & Bulling, A 2013, I know what you are reading - recognition of document types using mobile eye tracking. in ISWC 2013 - Proceedings of the 2013 ACM International Symposium on Wearable Computers. pp. 113-116, 2013 17th ACM International Symposium on Wearable Computers, ISWC 2013, Zurich, Switzerland, 13/9/9. https://doi.org/10.1145/2493988.2494354
Kunze KS, Utsumi Y, Shiga Y, Kise K, Bulling A. I know what you are reading - recognition of document types using mobile eye tracking. In ISWC 2013 - Proceedings of the 2013 ACM International Symposium on Wearable Computers. 2013. p. 113-116 https://doi.org/10.1145/2493988.2494354
Kunze, Kai Steven ; Utsumi, Yuzuko ; Shiga, Yuki ; Kise, Koichi ; Bulling, Andreas. / I know what you are reading - recognition of document types using mobile eye tracking. ISWC 2013 - Proceedings of the 2013 ACM International Symposium on Wearable Computers. 2013. pp. 113-116
@inproceedings{32f78705189747d39be0e99cf07611b9,
title = "I know what you are reading - recognition of document types using mobile eye tracking",
abstract = "Reading is a ubiquitous activity that many people even perform in transit, such as while on the bus or while walking. Tracking reading enables us to gain more insights about expertise level and potential knowledge of users - towards a reading log tracking and improve knowledge acquisition. As a first step towards this vision, in this work we investigate whether different document types can be automatically detected from visual behaviour recorded using a mobile eye tracker. We present an initial recognition approach that combines special purpose eye movement features as well as machine learning for document type detection. We evaluate our approach in a user study with eight participants and five Japanese document types and achieve a recognition performance of 74{\%} using user-independent training.",
keywords = "Document classification, Eye tracking, Reading behavior",
author = "Kunze, {Kai Steven} and Yuzuko Utsumi and Yuki Shiga and Koichi Kise and Andreas Bulling",
year = "2013",
doi = "10.1145/2493988.2494354",
language = "English",
isbn = "9781450321273",
pages = "113--116",
booktitle = "ISWC 2013 - Proceedings of the 2013 ACM International Symposium on Wearable Computers",

}

TY - GEN

T1 - I know what you are reading - recognition of document types using mobile eye tracking

AU - Kunze, Kai Steven

AU - Utsumi, Yuzuko

AU - Shiga, Yuki

AU - Kise, Koichi

AU - Bulling, Andreas

PY - 2013

Y1 - 2013

N2 - Reading is a ubiquitous activity that many people even perform in transit, such as while on the bus or while walking. Tracking reading enables us to gain more insights about expertise level and potential knowledge of users - towards a reading log tracking and improve knowledge acquisition. As a first step towards this vision, in this work we investigate whether different document types can be automatically detected from visual behaviour recorded using a mobile eye tracker. We present an initial recognition approach that combines special purpose eye movement features as well as machine learning for document type detection. We evaluate our approach in a user study with eight participants and five Japanese document types and achieve a recognition performance of 74% using user-independent training.

AB - Reading is a ubiquitous activity that many people even perform in transit, such as while on the bus or while walking. Tracking reading enables us to gain more insights about expertise level and potential knowledge of users - towards a reading log tracking and improve knowledge acquisition. As a first step towards this vision, in this work we investigate whether different document types can be automatically detected from visual behaviour recorded using a mobile eye tracker. We present an initial recognition approach that combines special purpose eye movement features as well as machine learning for document type detection. We evaluate our approach in a user study with eight participants and five Japanese document types and achieve a recognition performance of 74% using user-independent training.

KW - Document classification

KW - Eye tracking

KW - Reading behavior

UR - http://www.scopus.com/inward/record.url?scp=84885191491&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84885191491&partnerID=8YFLogxK

U2 - 10.1145/2493988.2494354

DO - 10.1145/2493988.2494354

M3 - Conference contribution

AN - SCOPUS:84885191491

SN - 9781450321273

SP - 113

EP - 116

BT - ISWC 2013 - Proceedings of the 2013 ACM International Symposium on Wearable Computers

ER -