Accepted author manuscript, 1.78 MB, PDF document
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography
AU - Bulling, Andreas
AU - Ward, Jamie A
AU - Gellersen, Hans
AU - Tröster, Gerhard
PY - 2008
Y1 - 2008
N2 - In this work we analyse the eye movements of people in transit in an everyday environment using a wearable electrooculographic (EOG) system. We compare three approaches for continuous recognition of reading activities: a string matching algorithm which exploits typical characteristics of reading signals, such as saccades and fixations; and two variants of Hidden Markov Models (HMMs) - mixed Gaussian and discrete. The recognition algorithms are evaluated in an experiment performed with eight subjects reading freely chosen text without pictures while sitting at a desk, standing, walking indoors and outdoors, and riding a tram. A total dataset of roughly 6 hours was collected with reading activity accounting for about half of the time. We were able to detect reading activities over all subjects with a top recognition rate of 80.2% (71.0% recall, 11.6% false positives) using string matching. We show that EOG is a potentially robust technique for reading recognition across a number of typical daily situations.
AB - In this work we analyse the eye movements of people in transit in an everyday environment using a wearable electrooculographic (EOG) system. We compare three approaches for continuous recognition of reading activities: a string matching algorithm which exploits typical characteristics of reading signals, such as saccades and fixations; and two variants of Hidden Markov Models (HMMs) - mixed Gaussian and discrete. The recognition algorithms are evaluated in an experiment performed with eight subjects reading freely chosen text without pictures while sitting at a desk, standing, walking indoors and outdoors, and riding a tram. A total dataset of roughly 6 hours was collected with reading activity accounting for about half of the time. We were able to detect reading activities over all subjects with a top recognition rate of 80.2% (71.0% recall, 11.6% false positives) using string matching. We show that EOG is a potentially robust technique for reading recognition across a number of typical daily situations.
U2 - 10.1007/978-3-540-79576-6_2
DO - 10.1007/978-3-540-79576-6_2
M3 - Conference contribution/Paper
SN - 978-3-540-79575-9
VL - 5013
SP - 19
EP - 37
BT - Lecture Notes in Computer Science
A2 - Indulska, J.
A2 - Patterson, D. J.
A2 - Rodden, T.
A2 - Ott, M.
PB - Springer
T2 - Pervasive Computing 2008
Y2 - 1 May 2008
ER -