TY  - CONF
ID  - bulling09_ubicomp
T1  - Eye Movement Analysis for Activity Recognition
A1  - Bulling, Andreas
A1  - Ward, Jamie A.
A1  - Gellersen, Hans
A1  - Tröster, Gerhard
TI  - Proc. of the 11th International Conference on Ubiquitous Computing (UbiComp 2009)
Y1  - 2009
SP  - 41
EP  - 50
PB  - ACM Press
CY  - Orlando, United States
N1  - acceptance rate: 12.4%
M2  - doi: 10.1145/1620545.1620552
KW  - Activity Recognition
KW  - Electrooculography (EOG)
KW  - Eye Motion Analysis
KW  - Recognition of Office Activities
N2  - 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.
ER  -