%J PERVASIVE
%L discovery1535735
%P 19-37
%I Springer, Berlin, Heidelberg
%C Sydney, Australia
%V 5013
%A A Bulling
%A JA Ward
%A H-W Gellersen
%A G Tröster
%S Lecture Notes in Computer Science
%T Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography
%B Proceedings of the 6th International Conference on Pervasive Computing: Pervasive 2008
%X 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.
%D 2008
%O This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
%K Hide Markov Model, Activity Recognition, String Match, Reading Activity, Baseline Drift