@INPROCEEDINGS {BWGT08,
  title = {Robust Recognition of Reading Activity in Transit Using
Wearable Electrooculography},
  author = {Bulling, Andreas and Ward, Jamie A.  and Gellersen, Hans-W.
and Tr\"oster, Gerhard},
  booktitle = {Proc. of the 6th International Conference on Pervasive
Computing (Pervasive 2008)},
  year = {2008},
  month = {May},
  pages = {19--37},
  location = {Sydney, Australia},
  doi = {10.1007/978-3-540-79576-6_2},
  keywords = {Activity Recognition, Wearable, Electrooculography, EOG,
Recognition of Reading, Transit, Reading Activity},
  abstract = {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.},
}