How Much Do You Read? – Counting the Number of Words
a User Reads Using Electrooculography
Kai Kunze, Masai Katsutoshi, Yuji Uema, Masahiko Inami
Graduate School of Media Design
Keio University, Yokohama, Japan
Email:{kai/masai/uema/inami}@kmd.keio.ac.jp
ABSTRACTWe read to acquire knowledge. Reading is a common activity performed in transit and while sitting, for example duringcommuting 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 stepto 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 onbody 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 anerror rate as low as 7% (based on eye motions alone) for theword count estimation (std = 0.5%).

Figure 1: Mockup of a service to quantify reading habits during everyday life.
people to read more and to adopt healthy reading habits ischallenging especially as the amount easy digestible content
in form of videos etc. increases. Automatically tracking physical activities can motivate users to more healthy lifestyles[2]. We believe this translates also to cognitive skills andtasks. We want to investigate wether we can track reading
habits similar to physical activity to give users tools to improve their mental fitness. Letters and words represent ideas
and concepts; tracking the volume, speed and time a user isreading them seems particularly valuable as it gives first insights into learning and provides us with a basic countablemeasure of our performance[7]. For example, children suffering from reading disabilities can be earlier diagnosed, people can without trouble improve their reading speed and older
adults have an easier way to fight dementia. Since researchsuggests that performance related to these situations is closely
linked to reading volume[15, 8].

Author Keywords

Eye Movement Analysis, EOG, Reading, Wordcount
ACM Classification Keywords

I.5.4 PATTERN RECOGNITION: Applications: Signal processing
INTRODUCTION

Increased reading volume is associated with numerous cognitive benefits, including improved vocabulary skills, higher
general knowledge and increased critical thinking [4]. Furthermore, reading is entertaining and has social value, higher
reading volumes in adolescents are correlated with higher selfesteem and improved cognitive and emotional well-being[13,14]. Although there are these strong positive effects, only
few previous works evaluated reading activities in situ andeven fewer tried to quantify them[3, 10]. Despite the growing
awareness of the importance of reading for learning, getting

We still have a hard time defining what healthy reading habits
for adults are[4], as tools are missing to quantify reading ineveryday situations and in long term studies. This paper provides the first steps towards assessing reading volume in realistic settings utilizing electrooculography. The particular
contributions of this work are (1) we present a method toquantify how much words a user reads using electrooculography working over different devices, diverse users and varying
text length (2) we achieve the lowest error rate of 7% userindependent for our word count estimation over two data setsof in total 6 users over 5 documents each of varying sizes(from 115-881 words). (3) So far nobody implemented aword counting algorithm using EOG. Also our EOG setup
differs significantly from related work focusing only on read-Permission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must behonored. For all other uses, contact the owner/author(s). Copyright is held by the author/owner(s).
AH ’15, March 09 - 11, 2015, Singapore, Singapore
Copyright is held by the owner/author(s). Publication rights licensed to ACM.
ACM 978-1-4503-3349-8/15/03. . . $15.00
http://dx.doi.org/10.1145/2735711.2735832

125ing detection, it uses only placements on the nose to infer
left/right and up/down movements. This setup could be easily integrated in smart glasses.
We see our work as a first step towards services that quantify
reading habits during everyday life (see Figure1).
APPROACH

Figure 3: EOG signal, horizontal component for a user reading a document. The line breaks (even the three shorter lines)
are easy to spot in the data.

Figure 2: Electrode Setup: setup similar to some related workon the left, new setup on the right.
As seen in Figure 2, our electrode setup differs substantiallyfrom systems in related work. Most EOG researcher use 5
electrodes: top and bottom, left and right for measurements
and another electrode for reference. We apply just 4 active
electrodes, two left and right from the nose, one between theeyes and the last one as reference also between the eyes. The
setup is inspired by J!NS MEME1 , a consumer EOG smart
glasses device. However, we could not find any specificationor publication related to the EOG setup of MEME that give
more detailed hardware information or could be cited as related work. Yet, from the information we gathered from J!NS
the biggest differences of our system to MEME are, 1) we use
4 active electrodes, meme employs 3 normal electrodes. Our
setup should be more resilient to noise and thus interestingfor future smart glasses. From the electrodes marked 1-2 weget the horizontal component of the eye movement, From theelectrodes 1/2 to the one between the eyes the vertical component.

Figure 4: The raw data for a segment of the horizontal EOG
component between two line-breaks. The median filter is depicted in blue and black crosses are the peaks detected as ”forward” saccades.

of the EOG signal from a user reading a document. The line
breaks (valleys) are easy visible. For reference we are using a AC system not DC, as most related work (there is nomuch practical difference except some properties of the sample signals might look strange to researchers only used to DCsystems).

We assume the signal is pre-segmented in reading and notreading segments using available reading detection algorithms [3]. We assume reading detection is more or less asolved problem. After reading detection we apply first a line
break detection and then 3 different types of words read estimations. We will go in detail in the following.

Words Read Estimation

After line break detection, we apply words read estimation.
Static Word Count – The easiest algorithm to determine howmany words a person read is to multiply the line breaks witha static word count per line (we use 9.5 words determined
experimentally).

Line Break Detection

We use a simple valley detection algorithm to detect line
breaks on the horizontal signal component of the EOG. We
define a point A as a minimum if it has the minimum value in
it’s vicinity, and was preceded (to the left) by a value lower byDelta (Delta is defined experimentally by 60 ms). We also apply a minimum size threshold for the valley detection (the 5%
percentile also defined experimentally). In Figure3 we see thepeak detection algorithm applied to the horizontal component
1Line-Break SVR Word Count – A more advanced algorithm
uses only line-break saccade features. From a selection of 25features, we determined the average length of line-break saccades, minimum length of saccades, time between line-breaksaccades as the best estimators for word count on this level.
We use this features to train an Support Vector Regression(SVR) algorithm with radial base kernel function to predict
the words read.

https://www.jins-jp.com/jinsmeme/en/

126

(a)

(b)

(c)

(d)

Figure 5: Pictures from the data recordings. Participants are equipped with the Polymate EOG and read on the different devices:
paper, 8” tablet, laptop and 9” tablet.
square design. Before starting the experiments, the subjects
attached the 4 electrodes on their faces. After that, the experimenter explained to the subject what to do and instructed
them. Once recording started, the subject began to read andthe experimenter observed the procedure on laptop. Each document was recorded separately. The document order was notchanged depending on each subject. Photos from the recording are shown in Figure5. All recordings were performed in
an indoor university environment.
Figure 6: Samples from the electrode setup used.

RESULTS AND DISCUSSION

Applying the methods in the approach on the collected datafrom the 6 participants, we reach the following results. We
compare our inference to a word count estimate derived froma perfect reading detection system for baseline (most researchin the related work focuses on it). We estimate the number ofwords a person was reading just based on time and compare
this to our system.

Line-Features SVR Count – The most advanced algorithm
takes also the features from the sections between line-breaks.
We apply a Median filter with length 50 ms to these sections
and apply a peak detection algorithm (analogous to the valley
detection for the line-breaks) to get the forward saccades (forward in terms of reading direction) see Fig.4. Then we calculate the following forward-saccade features (also selected outof around 25 features): number of forward saccades, lengthof saccades, time between saccades. As before we use linebreak saccade features and additionally the forward saccadefeatures to train the SVR.

MethodTime Baseline
Static Word Count
Line-Break SVR Word Count
Line-Features SVR Word Count

Error Rate
31%
16%
10%
7%

STD
9%
3%
1%
0.5%

EXPERIMENTS

Table 1: Overview of the word count estimation error andstandard deviation of the error for different methods. Firstthe baseline just using the time a user read a text, second
is a static word number times the detected line-breaks, thirda SVR based on Line Break features alone, and last a SVR
based on Line Break and Line features.

We used the Polymate mini to track eye movements. It’sa EOG device with active electrodes. This device detects
users eye movement by measuring EOG via attached activeelectrodes. The Figures2 and 6 show how to attach the electrodes.For detecting a horizontal movement, we used the difference between EOD data from electrode 1 and 2. For detecting a vertical movement, we used the difference between
EOD data from electrode 1 and one between the eyes. ThePolymate is connected via Bluetooth technology to laptop
where experimenter can see the EOG data. The sampling rate
recorded by the device was 1K Hz.
We have 6 volunteers with multinational background( 2 Canadian, 1 Syrian, 1 Indonesian, 1 French and 1 Japanese), allstudents, 3 female, average age 24.3. Each subject read 5
documents with different lengths: 115, 253, 519, 679 and881 words in ascending order. We prepared 4 different media types which had different width and height sizes, but theirfont size - 12pt - was constant. The media types are A4 paper,tablet,iPad mini and laptop. The media types are our independent variables, we cycle through them using a lattice

For the Line break detection we have an error of 5%, std 1.2%.
The summary of the results can be found in Table1. The static
word count method already performs with around half the error of the time baseline (16%). Interestingly, all methods are
significantly better than baseline, p-values comparing them tobaseline: static 0.04 (F = 1.2), line-break 0.02 (F= 0.79), linefeatures 0.009 (F=0.67).
RELATED WORKAs we are interested in tracking reading habits, a collectionof cognitive tasks, we first might try direct brain sensing. Yet,as realted work shows most methods are too bulky or are too127

noisy to get decent results related to recognizing reading [11,5].

journal of the American Medical Association 298, 19(2007), 2296–2304.

The most interesting modalities for direct brain sensing seemto be electroencephalography (EEG) and near-infrared spectroscopy (NIRS), as both can be used in mobile settings[17].
However, for both spacial and for NIRS temporal resolution are not so good. Their signal is also strongly affectedby motion noise and usually requires complex filtering/preprocessing steps.

3. Bulling, A., Ward, J. A., and Gellersen, H. Multimodal
Recognition of Reading Activity in Transit Using
Body-Worn Sensors. ACM Trans. on Applied Perception
9, 1 (2012), 2:1–2:21.

However, the strong relationship between reading and eye
movements is very well explored in cognitive science and psychology [16, 9]. Most of the reading research in psychology
however emphasizes on older adults or disabled [5].There are
only a few research publications centering around reading detection in mobile and stationary settings [3]. As such detecting reading can be used as a very simple word counting mechanism, as there’s a relation between time read and the readvolume. Biedert et al. look into how people read text. They
presented a method to discriminate skimming from reading
using a novel set of eye movement features [1].

5. Ferstl, E. C., Neumann, J., Bogler, C., and von Cramon,
D. Y. The extended language network: A meta-analysisof neuroimaging studies on text comprehension. HumanBrain Mapping (2008), 581–593.

4. Cunningham, A., and Stanovich, K. What reading does
for the mind. Journal of Direct Instruction 1, 2 (2001),
137–149.

6. Genesee, F., Lindholm-Leary, K., Saunders, W., andChristian, D. English language learners in us schools:
An overview of research findings. Journal of Educationfor Students Placed at Risk 10, 4 (2005), 363–385.
7. Halpern, D. F. Teaching for critical thinking: Helping
college students develop the skills and dispositions of a
critical thinker. New directions for teaching and learning
1999, 80 (1999), 69–74.

Manabe et al. also explore different EOG placement for electrodes using a headphone type formfactor [12].

8. Hansen, D. M. A discourse structure analysis of thecomprehension of rapid readers.

Concerning reading habits, there are some questionnaire
based evaluations giving advice about effective reading techniques to second language learners, as well as for children
with reading disabilities and older adults struggling with dementia[6, 15]. Hansen [8] reports on a series of studies onreading comprehension with rapid readers trained in the Evelyn Wood method.

9. Irwin, D. E. Fixation location and fixation duration asindices of cognitive processing. The interface oflanguage, vision, and action: Eye movements and thevisual world (2004), 105–134.
10. Kunze, K., Kawaichi, H., Yoshimura, K., and Kise, K.
The wordmeter – estimating the number of words readusing document image retrieval and mobile eye tracking.
In Proc. ICDAR 2013 (2013).

The closest to our work is the Wordometer implemented byKunze et al. [10]. They introduce a word counting algorithm
based on mobile eye tracking. They use an optical system, amobile eye tracker and they utilize the scene camera of thetracker justify the eye gaze. Our system is easier to deploy(e.g. potentially intergratible into glasses) and has less battery constraints. As far as we know, this is the only research
work exploring technology support to quantify reading andpresenting a word count estimation algorithm capable of dealing with varying device types, line lengths and different reading speeds.

11. Kunze, K., Yuki, S., Ishimaru, S., and Kise, K. Reading
activity recognition using an off-the-shelf eeg. In Proc.
ICDAR 2013 (2013).
12. Manabe, H., and Fukumoto, M. Full-time wearable
headphone-type gaze detector. In CHI’06 Extended
Abstracts on Human Factors in Computing Systems,ACM (2006), 1073–1078.

CONCLUSION

13. Moore, D. W., Alvermann, D. E., and Hinchman, K. A.
Struggling Adolescent Readers: A Collection ofTeaching Strategies. ERIC, 2000.

We presented our work towards tracking how much a user
reads, enabling quantified feedback about reading volume.
We show an word count estimation algorithm that works with7%. We believe this is a good start for real world reading
tracking systems, of course we need a more representative
data set and in a next step we want to get rid of the taped electrode placement, integrating the electrodes in a smart glasses
frame, to enable a quantified reading service as shown in Figure 1.

14. Moore, R. L., and Moschis, G. P. Impact of newspaper
reading on adolescent consumers. Newspaper Research
Journal 2, 4 (1981), 3–8.
15. Rashotte, C. A., and Torgesen, J. K. Repeated reading
and reading fluency in learning disabled children.
Reading Research Quarterly (1985), 180–188.
16. Spache, G. D. Is this a breakthrough in reading? The
Reading Teacher 15, 4 (1962), 258–263.

REFERENCES

1. Biedert, R., Hees, J., Dengel, A., and Buscher, G. A
robust realtime reading-skimming classifier. In Proc.
ETRA 2012 (2012), 123–130.
2. Bravata, D. M., and Smith-Spangler. Using pedometers
to increase physical activity and improve health. The

17. Tsunashima, H., Yanagisawa, K., and Iwadate, M.
Measurement of brain function using near-infrared
spectroscopy (nirs), 2012.

128