TY - GEN
T1 - Quantifying reading habits - counting how many words you read
AU - Kunze, Kai
AU - Masai, Katsutoshi
AU - Inami, Masahiko
AU - Sacakli, Omer
AU - Liwicki, Marcus
AU - Dengel, Andreas
AU - Ishimaru, Shoya
AU - Kise, Koichi
N1 - Publisher Copyright:
© 2015 ACM.
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
T3 - UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
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
T2 - 3rd ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015
Y2 - 7 September 2015 through 11 September 2015
ER -