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How much do you read?: counting the number of words a user reads using electrooculography

Authors:

Kai Kunze,

Masai Katsutoshi,

Yuji Uema,

Masahiko InamiAuthors Info & Claims

AH '15: Proceedings of the 6th Augmented Human International Conference

Pages 125 - 128

https://doi.org/10.1145/2735711.2735832

Published: 09 March 2015 Publication History

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Abstract

We read to acquire knowledge. Reading is a common activity performed in transit and while sitting, for example during commuting to work or at home on the couch. Although reading is associated with high vocabulary skills and even with increased critical thinking, we still know very little about effective reading habits. In this paper, we argue that the first step to understanding reading habits in real life we need to quantify them with affordable and unobtrusive technology. Towards this goal, we present a system to track how many words a user reads using electrooculography sensors. Compared to previous work, we use active electrodes with a novel on-body placement optimized for both integration into glasses (or head-worn eyewear etc) and for reading detection. Using this system, we present an algorithm capable of estimating the words read by a user, evaluate it in an user independent approach over experiments with 6 users over 4 different devices (8" and 9" tablet, paper, laptop screen). We achieve an error rate as low as 7% (based on eye motions alone) for the word count estimation (std = 0.5%).

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Cited By

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Mifsud MCamilleri TCamilleri K(2023)Training an EOG-Based Wordometer Without Reading – A Simple HCI Application to Quantify Reading MetricsHCI International 2023 Posters10.1007/978-3-031-35989-7_47(366-373)Online publication date: 9-Jul-2023
https://doi.org/10.1007/978-3-031-35989-7_47
Grabny ŁDoniec RSieciński SPiaseczna NDuraj K(2022)The Application for Reading Comprehension and Reading Speed TestInnovations in Biomedical Engineering10.1007/978-3-030-99112-8_25(245-254)Online publication date: 1-Jun-2022
https://doi.org/10.1007/978-3-030-99112-8_25
Hasegawa THatakenaka T(2019)Touch-Typing Detection Using Eyewear: Toward Realizing a New Interaction for Typing ApplicationsSensors10.3390/s1909202219:9(2022)Online publication date: 30-Apr-2019
https://doi.org/10.3390/s19092022
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Index Terms

How much do you read?: counting the number of words a user reads using electrooculography
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems

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Reviews

Reviewer: Lalit P Saxena

Reading improves the reader's vocabulary usage and understanding of the subject, which further enhances thinking power. The reading proficiency of a person can be different from others'; it can be estimated with the word count each reader reads in a given reading time. This paper uses electrooculography to estimate the words read count. The authors developed a system using electrooculography sensors to count the number of words read by a user. For reading detection, the proposed system uses portable electrodes that can be placed on a set of eyewear or glasses. This system uses four electrodes instead of the five used in other systems. It works in two phases, line break detection and words read estimation. The authors compared four methods: time baseline, static word count, line-break support vector regression (SVR) word count, and line-features SVR word count. For the experiments, the authors employed six students-two Canadians, one Syrian, one Indonesian, one French, and one Japanese-with an average age of 24.3 years, including three females. Each student read five documents comprising 115, 253, 519, 679, and 881 words with a font size of 12 points over four different media types: 8-inch tablet, 9-inch tablet, A4 paper, and laptop screen. The system reports a seven percent error rate for eye motions alone with the standard deviation of 0.5 percent for word count estimation. The authors believe that this system is an initial prototype for real-world reading tracking systems. In future enhancements, this system would be equipped with the in-built electrodes in the frames of the smart glasses. Online Computing Reviews Service

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Information & Contributors

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Published In

AH '15: Proceedings of the 6th Augmented Human International Conference

March 2015

241 pages

ISBN:9781450333498

DOI:10.1145/2735711

General Chairs:
Suranga Nanayakkara
Singapore University of Technology and Design (Singapore)
,
Ellen Yi-Luen Do
Georgia Institute of Technology & National University of Singapore (Singapore)
,
Program Chairs:
Jun Rekimoto
The University of Tokyo (Japan)
,
Jochen Huber
MIT Media Lab & Singapore University of Technology and Design (Singapore)
,
Bing-Yu Chen
National Taiwan University (Taiwan)

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 March 2015

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AH '15

AH '15: The 6th Augmented Human International Conference

March 9 - 11, 2015

Singapore, Singapore

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Overall Acceptance Rate 121 of 306 submissions, 40%

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Cited By

View all

Mifsud MCamilleri TCamilleri K(2023)Training an EOG-Based Wordometer Without Reading – A Simple HCI Application to Quantify Reading MetricsHCI International 2023 Posters10.1007/978-3-031-35989-7_47(366-373)Online publication date: 9-Jul-2023
https://doi.org/10.1007/978-3-031-35989-7_47
Grabny ŁDoniec RSieciński SPiaseczna NDuraj K(2022)The Application for Reading Comprehension and Reading Speed TestInnovations in Biomedical Engineering10.1007/978-3-030-99112-8_25(245-254)Online publication date: 1-Jun-2022
https://doi.org/10.1007/978-3-030-99112-8_25
Hasegawa THatakenaka T(2019)Touch-Typing Detection Using Eyewear: Toward Realizing a New Interaction for Typing ApplicationsSensors10.3390/s1909202219:9(2022)Online publication date: 30-Apr-2019
https://doi.org/10.3390/s19092022
Schäfer JGebhard MMakedon F(2019)Feasibility analysis of sensor modalities to control a robot with eye and head movements for assistive tasksProceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3316782.3322774(482-488)Online publication date: 5-Jun-2019
https://dl.acm.org/doi/10.1145/3316782.3322774
Ndibwile JLuhanga EFall DMiyamoto DBlanc GKadobayashi Y(2019)An Empirical Approach to Phishing Countermeasures Through Smart Glasses and Validation AgentsIEEE Access10.1109/ACCESS.2019.29406697(130758-130771)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2940669
Lagodzinski PShirahama KGrzegorzek M(2018)Codebook-based electrooculography data analysis towards cognitive activity recognitionComputers in Biology and Medicine10.1016/j.compbiomed.2017.10.02695:C(277-287)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1016/j.compbiomed.2017.10.026
Sanchez SDingler TGu HKunze KKaye JDruin ALampe CMorris DHourcade J(2016)Embodied ReadingProceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems10.1145/2851581.2892353(1459-1466)Online publication date: 7-May-2016
https://dl.acm.org/doi/10.1145/2851581.2892353
Banerjee ARakshit ATibarewala D(2016)Application of Electrooculography to estimate word count while reading text2016 International Conference on Systems in Medicine and Biology (ICSMB)10.1109/ICSMB.2016.7915115(174-177)Online publication date: 2016
https://doi.org/10.1109/ICSMB.2016.7915115
Cena FLikavec SRapp AMase KLangheinrich MGatica-Perez DGellersen HChoudhury TYatani K(2015)Quantified self and modeling of human cognitionAdjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers10.1145/2800835.2800954(1021-1026)Online publication date: 7-Sep-2015
https://dl.acm.org/doi/10.1145/2800835.2800954
Kunze KMasai KInami MSacakli ÖLiwicki MDengel AIshimaru SKise KMase KLangheinrich MGatica-Perez DGellersen HChoudhury TYatani K(2015)Quantifying reading habitsProceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/2750858.2804278(87-96)Online publication date: 7-Sep-2015
https://dl.acm.org/doi/10.1145/2750858.2804278
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Recommendations

Quantifying reading habits: counting how many words you read

Understanding How Low Vision People Read Using Eye Tracking

Multimodal recognition of reading activity in transit using body-worn sensors

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