TY  - CONF
ID  - bulling08_pervasive
T1  - Robust {R}ecognition of {R}eading {A}ctivity in {T}ransit {U}sing {W}earable {E}lectrooculography
A1  - Bulling, Andreas
A1  - Ward, Jamie A.
A1  - Gellersen, Hans
A1  - Tröster, Gerhard
TI  - Proc. of the 6th International Conference on Pervasive Computing (Pervasive 2008)
Y1  - 2008
SP  - 19
EP  - 37
PB  - Springer
CY  - Sydney, Australia
SN  - 978-3-540-79575-9
SN  - 0302-9743 (Print) 1611-3349 (Onl
N1  - acceptance rate: 15.8%
M2  - doi: 10.1007/978-3-540-79576-6_2
KW  - Activity Recognition
KW  - Electrooculography (EOG)
KW  - Reading Activity
KW  - Recognition of Reading
KW  - Transit
KW  - wearable
N2  - 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.
ER  -