Online trajectory classification

Sas, Corina and O'Hare, Gregory and Reilly, Ronan (2003) Online trajectory classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2659. pp. 1035-1044. ISSN 0302-9743

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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.

Item Type: Journal Article
Journal or Publication Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1700
Subjects:
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 134944
Deposited By: ep_importer_pure
Deposited On: 24 Jun 2019 09:15
Refereed?: Yes
Published?: Published
Last Modified: 07 Jan 2020 06:54
URI: https://eprints.lancs.ac.uk/id/eprint/134944

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