%Aigaion2 BibTeX export from IfE Document Management System
%Monday 21 May 2012 10:17:14 AM

@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 Recognition of Reading Activity in Transit Using Wearable Electrooculography},
  booktitle = {Proc. of the 6th International Conference on Pervasive Computing (Pervasive 2008)},
     series = {Lecture Notes in Computer Science},
     volume = {5013},
       year = {2008},
      pages = {19--37},
  publisher = {Springer},
       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.}
}