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On-line trajectory classification

Research output: Contribution in Book/Report/ProceedingsChapter

Published

Publication date2003
Host publicationInternational Conference on Computational Science, Workshop on Scientific Visualisation and Human-Machine Interaction in a Problem-Solving Environment.
PublisherSpringer Verlag
Pages1035-1044
Number of pages10
VolumeLNCS 2
Original languageEnglish

Abstract

This study proposes a modular system for clustering on-line motion trajectories obtained while users navigate within a virtual environment. It presents a neural network simulation that gives a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organizing map algorithm was tested and improved to above 85% by using learning vector quantization. The benefits of this approach and the possibility of extending the methodology to the study of navigation in Human Computer Interaction are discussed.