Eye Movement Analysis for Activity Recognition Using Electrooculography
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Transportation mode detection using mobile phones and GIS information
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Look & touch: gaze-supported target acquisition
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GeoGazemarks: providing gaze history for the orientation on small display maps
Proceedings of the 14th ACM international conference on Multimodal interaction
Gaze map matching: mapping eye tracking data to geographic vector features
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Interpreting maps through the eyes of expert and novice users
International Journal of Geographical Information Science
The influence of gaze history visualization on map interaction sequences and cognitive maps
Proceedings of the 1st ACM SIGSPATIAL International Workshop on MapInteraction
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The spatio-temporal characteristics of eye movements vary according to the activity the user of a cartographic map is performing. In this paper, we use these eye movement characteristics to automatically detect the map user's activity, an approach with great potential in gaze-assistive map interfaces. A dataset of 587 eye movement recordings from 17 participants was used to train and cross-validate a support vector machine (SVM) classifier over 229 features. The classifier can distinguish 6 common map activities with an accuracy of approx. 78%.