%Aigaion2 BibTeX export from dms.andreas-bulling.de
%Friday 10 September 2010 07:23:20 PM

@INPROCEEDINGS{bulling09_ubicomp,
     author = {Bulling, Andreas and Ward, Jamie A. and Gellersen, Hans and Tr{\"{o}}ster, Gerhard},
   keywords = {Activity Recognition, Electrooculography (EOG), Eye Motion Analysis, Recognition of Office Activities},
      month = sep,
      title = {Eye Movement Analysis for Activity Recognition},
  booktitle = {Proc. of the 11th International Conference on Ubiquitous Computing (UbiComp 2009)},
       year = {2009},
      pages = {41--50},
  publisher = {ACM Press},
   location = {Orlando, United States},
       note = {acceptance rate: 12.4\%},
        doi = {10.1145/1620545.1620552},
   abstract = {In this work we investigate eye movement analysis as a new modality for recognising human activity. We devise 90 different features based on the main eye movement characteristics: saccades, fixations and blinks. The features are derived from eye movement data recorded using a wearable electrooculographic (EOG) system. We describe a recognition methodology that combines minimum redundancy maximum relevance feature selection (mRMR) with a support vector machine (SVM) classifier. We validate the method in an eight participant study in an office environment using five activity classes: copying a text, reading a printed paper, taking hand-written notes, watching a video and browsing the web. In addition, we include periods with no specific activity. Using a person-independent (leave-one-out) training scheme, we obtain an average precision of 76.1\% and recall of 70.5\% over all classes and participants. We discuss the most relevant features and show that eye movement analysis is a rich and thus promising modality for activity recognition.}
}