Kai Kunze
Masai Katsutoshi
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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Index Terms
- How much do you read?: counting the number of words a user reads using electrooculography
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Reviews
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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