UBICOMP/ISWC ’16 ADJUNCT, SEPTEMBER 12-16, 2016, HEIDELBERG, GERMANY

The Wordometer 2.0 – Estimating theNumber of Words You Read in RealLife using Commercial EOG Glasses
Shoya Ishimaru
German Research Center for
Artificial Intelligence
Kaiserslautern, Germany
Shoya.Ishiamru@dfki.de

Koichi Kise
Osaka Preecture University
Sakai, Japan
kise@cs.osakafu-u.ac.jp

Kai Kunze
Keio University
Yokohama, Japan
kai@kmd.keio.ac.jp

Andreas Dengel
German Research Center for
Artificial Intelligence
Kaiserslautern, Germany
Andreas.Dengel@dfki.deAbstract

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 aday. 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 anaffordable 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.

Author Keywords

Eye movement; electrooculography; reading; quantified self

ACM Classification Keywords

H.5.m [Information interfaces and presentation (e.g., HCI)]:
MiscellaneousIntroduction
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Copyright is held by the owner/author(s).
Ubicomp/ISWC’16 Adjunct, September 12–16, 2016, Heidelberg, Germany.
ACM 978-1-4503-4462-3/16/09.
http://dx.doi.org/10.1145/2968219.2971398

293

The relationship between cognitive benefits (e.g. vocabulary skills, academic scores) and reading habits, especially
increased reading volumes is well-explored in the study ofeducation and cognitive science [2]. As people can be motivated to be physically fit by monitoring step counts [5], webelieve that counting the number of words they read canhelp them improve their daily reading volumes. In this pa-UBICOMP/ISWC ’16 ADJUNCT, SEPTEMBER 12-16, 2016, HEIDELBERG, GERMANY

Figure 1: Overview of the application which counts the number ofwords a user read. He is wearing J!NS MEME.

per, we present the read word counting system which works
on J!NS MEME, commercial EOG glasses (cf. Figure 1).
Quantifying reading activity is more difficult than tracking
step counts because reading is not a physical activity, and
it is hard to apply the same approach using body-mounted
motion sensors. Additional sensors are needed to recognize reading activity. We employ an eye tracking approach
to solve the problem. There is an array of research in psychology and cognitive science investigating the correlation
between eye movements and cognitive activities including reading [6]. Furthermore, technologies for tracking eye
movements are more and more pervasive these days [1].
The contribution of this paper is to show that (1) sensor signals from J!NS MEME are good enough to detect specificeye movements during reading (forward- and backwardsaccades) and (2) the number of read words can be estimated by features from the forward- and backward-saccades.
We have achieved an error rate of word counting algorithm
11% with user-independent training and 3.0% with userdependent training. This research has been approved by
the ethical committee of Osaka Prefecture University.

294

Figure 2: A screenshot image of the application to look back daily
reading habits. The word counting application on iPhone sends
the data to a web server, and it visualizes durations, word counts
and the reading speeds of whole day.

Wordometer 2.0

The idea of counting read words has been proposed by
Kunze et al. as "Wordometer” [4]. They have introduced
word counting algorithms based on mobile eye tracking
glasses and medical EOG sensors. However, although
their goal is to track daily reading habits, their setups are
too bulky to be worn regularly (e.g., the devices are expensive; battery lives are not enough to cover a whole day; andcables on devices prevent a user from moving naturally).
Compared to the previous work, this research aims to quantify reading activities with technologies that are completelyaffordable. We utilize commercial EOG glasses because
they are inexpensive, do not require significant user load,and have an long enough battery life for all-day use [3]. Additionally, there is no limitation to use the device from theviewpoint of privacy because it doesn’t equip a camera.
Features for the estimation are optimized for the device. We
define our new word counting system which is designed foreveryday use as “Wordometer 2.0.”

SESSION: DEMOS

Figure 3: The settings ofelectrodes to calculate EOG
vertical and horizontal component
on J!NS MEME.

Figure 4: An overview of theexperimental setup. Timestamps ofstart/stop reading paragraphs arerecorded according to scroll eventson iPad.

Owing to its advantages, the system can record natural
reading activities during the whole day. Therefore, it enables a user to know and review his/her reading from along-term view. The tracking result will be summarized
and visualized as shown in Figure 2. Reading speeds canalso be calculated by volumes in a period. By analyzing therhythm of the reading speed, the system should be able totell a user when the time to concentrate on reading was andin which part of a book did he/she was interested.

Word counting method

The word-counting method consists of three processes: obtaining a user’s eye movements, detecting forward/backwardsaccades, and estimating the number of words he/she read.
One reference electrode and two active electrodes areequipped on J!NS MEME as shown in Figure 3. The EOG
vertical component is calculated as an average of L andR, and the horizontal component is calculated as the difference between L and R. Figure 5 shows an overview of
the EOG horizontal component in a one-minute recordingthat includes reading activity. Negative values represent eye
movement right to left, and positive values represent left
to right. Regular patterns of eye movement appear during
reading activity because of line breaks.
Figure 5 shows outputs of the algorithm of detecting forwardand backward-saccades. Peak detection for forward-saccades
is applied after applying a median filter to remove noises.
Backward-saccades are detected if the sensor value islower than a threshold. The threshold is calculated dynamically as the difference between the mean and variance ofsensor values in a small window. The window size is one
second, which was decided experimentally.
The number of words a user read is estimated by support
vector regression. Four features are calculated for the re-295

Figure 5: EOG sensor signal during reading activity. Circle andtriangle markers are outputs of forward/backward-saccadesdetection.

gression: the total number of forward-saccades, the mean
EOG signal value of forward-saccades, the total numberof backward-saccades, and the mean EOG signal value ofbackward-saccades.

Evaluation

To evaluate the word-counting algorithm, we asked fivesubjects to read English essays on an iPad wearing J!NSMEME (cf. Figure 4). Every subject read 38 paragraphs,
so the total amount of paragraphs in the dataset was 190(minimum: 27 words; maximum: 120 words; average: 60
words in one paragraph). Because we used a prototype
device, the sampling frequency of the EOG signal was 11Hz. Training and testing were done by leave-one-subjectout as a user-independent approach. We evaluated errors
in the estimations with two measurements. One is an average of absolute error rates for each paragraph. This evaluation is valid for short-term recordings involving reading
speed estimation. The other is an absolute error throughUBICOMP/ISWC ’16 ADJUNCT, SEPTEMBER 12-16, 2016, HEIDELBERG, GERMANY

Acknowledgements

Table 1: Word count estimation errors
user-independentuser-dependentPar.

each paragraphall paragrapheach paragraphall paragraph

a17 %

11 %

14 %

2.2 %

b24 %

9.5 %

25 %

6.4 %

c24 %

18 %

15 %

2.4 %

d15 %

7.5 %

15 %

1.8 %

e15 %

11 %

12 %

2.4 %

Ave.

18 %

11 %

16 %

3.0 %

all paragraphs, which is calculated as the total error of allrecordings. This evaluation is valid for long-term recordings
including a total count of words read in a day.
The estimation errors are shown in Table 1. An average
error of five subjects during the readings for each paragraph was 18% and decreased to 11% when extended
with all 38 paragraphs. We also evaluated the algorithmwith a user-dependent approach (10-fold cross-validationfor each subject’s data), and the average error was 16% foreach paragraph and 3.0% through all paragraphs. The results represent the fact that eye movement during readingis user-dependent, and estimation errors will decrease bytraining with a user’s behavior as he/she uses the systemevery day.

Conclusion and Future work

This paper described the word count estimating method
and the summary visualization to increase daily reading
volumes. The system works on commercial EOG glasses
that are designed for everyday use. Future work includes
recording long-term data in a realistic setup, and investigating effective feedbacks to improve a user’s reading habits.

296This work is supported in part by JST CREST and JSPS
KAKENHI. Grant Numbers 25240028 and 15K12172.

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