Abstract
Reading is a very common learning activity, a lot of people perform it everyday even while standing in the subway or waiting in the doctors office. However, we know little about our everyday reading habits, quantifying them enables us to get more insights about better language skills, more effective learning and ultimately critical thinking. This paper presents a first contribution towards establishing a reading log, tracking how much reading you are doing at what time. We present an approach capable of estimating the words read by a user, evaluate it in an user independent approach over 3 experiments with 24 users over 5 different devices (e-ink reader, smartphone, tablet, paper, computer screen).We achieve an error rate as low as 5% (using a medical electrooculography system) or 15% (based on eye movements captured by optical eye tracking) over a total of 30 hours of recording. Our method works for both an optical eye tracking and an Electrooculography system. We provide first indications that the method works also on soon commercially available smart glasses.
| Original language | English |
|---|---|
| Title of host publication | UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 87-96 |
| Number of pages | 10 |
| ISBN (Print) | 9781450335744 |
| DOIs | |
| Publication status | Published - 2015 Sep 7 |
| Externally published | Yes |
| Event | 3rd ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015 - Osaka, Japan Duration: 2015 Sep 7 → 2015 Sep 11 |
Other
| Other | 3rd ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015 |
|---|---|
| Country | Japan |
| City | Osaka |
| Period | 15/9/7 → 15/9/11 |
Fingerprint
Keywords
- Electrooculography
- Eye Movement Analysis
- Mobile Eye tracking
- Quantifying Reading
- Reading Behavior
ASJC Scopus subject areas
- Computer Networks and Communications
- Hardware and Architecture
- Software
Cite this
Quantifying reading habits - counting how many words you read. / Kunze, Kai Steven; Masai, Katsutoshi; Inami, Masahiko; Sacakli, Omer; Liwicki, Marcus; Dengel, Andreas; Ishimaru, Shoya; Kise, Koichi.
UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, 2015. p. 87-96.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Quantifying reading habits - counting how many words you read
AU - Kunze, Kai Steven
AU - Masai, Katsutoshi
AU - Inami, Masahiko
AU - Sacakli, Omer
AU - Liwicki, Marcus
AU - Dengel, Andreas
AU - Ishimaru, Shoya
AU - Kise, Koichi
PY - 2015/9/7
Y1 - 2015/9/7
N2 - Reading is a very common learning activity, a lot of people perform it everyday even while standing in the subway or waiting in the doctors office. However, we know little about our everyday reading habits, quantifying them enables us to get more insights about better language skills, more effective learning and ultimately critical thinking. This paper presents a first contribution towards establishing a reading log, tracking how much reading you are doing at what time. We present an approach capable of estimating the words read by a user, evaluate it in an user independent approach over 3 experiments with 24 users over 5 different devices (e-ink reader, smartphone, tablet, paper, computer screen).We achieve an error rate as low as 5% (using a medical electrooculography system) or 15% (based on eye movements captured by optical eye tracking) over a total of 30 hours of recording. Our method works for both an optical eye tracking and an Electrooculography system. We provide first indications that the method works also on soon commercially available smart glasses.
AB - Reading is a very common learning activity, a lot of people perform it everyday even while standing in the subway or waiting in the doctors office. However, we know little about our everyday reading habits, quantifying them enables us to get more insights about better language skills, more effective learning and ultimately critical thinking. This paper presents a first contribution towards establishing a reading log, tracking how much reading you are doing at what time. We present an approach capable of estimating the words read by a user, evaluate it in an user independent approach over 3 experiments with 24 users over 5 different devices (e-ink reader, smartphone, tablet, paper, computer screen).We achieve an error rate as low as 5% (using a medical electrooculography system) or 15% (based on eye movements captured by optical eye tracking) over a total of 30 hours of recording. Our method works for both an optical eye tracking and an Electrooculography system. We provide first indications that the method works also on soon commercially available smart glasses.
KW - Electrooculography
KW - Eye Movement Analysis
KW - Mobile Eye tracking
KW - Quantifying Reading
KW - Reading Behavior
UR - http://www.scopus.com/inward/record.url?scp=84960911896&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84960911896&partnerID=8YFLogxK
U2 - 10.1145/2750858.2804278
DO - 10.1145/2750858.2804278
M3 - Conference contribution
AN - SCOPUS:84960911896
SN - 9781450335744
SP - 87
EP - 96
BT - UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PB - Association for Computing Machinery, Inc
ER -