Using eye movements to recognize activities on cartographic maps

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%.