%Aigaion2 BibTeX export from dms.andreas-bulling.de
%Friday 10 September 2010 07:43:46 PM

@INPROCEEDINGS{bulling08_pervasive,
     author = {Bulling, Andreas and Ward, Jamie A. and Gellersen, Hans and Tr{\"{o}}ster, Gerhard},
   keywords = {Activity Recognition, Electrooculography (EOG), Reading Activity, Recognition of Reading, Transit, wearable},
      month = may,
      title = {Robust {R}ecognition of {R}eading {A}ctivity in {T}ransit {U}sing {W}earable {E}lectrooculography},
  booktitle = {Proc. of the 6th International Conference on Pervasive Computing (Pervasive 2008)},
       year = {2008},
      pages = {19--37},
  publisher = {Springer},
   location = {Sydney, Australia},
       note = {acceptance rate: 15.8\%},
       issn = {0302-9743 (Print) 1611-3349 (Onl},
       isbn = {978-3-540-79575-9},
        doi = {10.1007/978-3-540-79576-6_2},
   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.}
}