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A typology of course of motion in simulated environments based on Bézier curve analysis

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Abstract

This paper proposes a novel method of analysing trajectories followed by people while they perform navigational tasks. The results indicate that modelling trajectories with Bézier curves provides a basis for the diagnosis of navigational patterns. The method offers five indicators: goodness of fit, average curvature, number of inflexion points, lengths of straight line segments, and area covered. Study results, obtained in a virtual environment show that these indicators carry important information about user performance, specifically spatial knowledge acquisition.

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Authors and Affiliations

  1. Computing Department, InfoLab21, Lancaster University, Lancaster, LA1 4WA, UK

    Corina Sas

  2. School of Computer Science and Informatics, University College Dublin, Belfield, Dublin, 4, Ireland

    Nikita Schmidt

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  1. Corina Sas
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  2. Nikita Schmidt
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Correspondence to Corina Sas.

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Corina Sas is a Lecturer in the field of human–computer interaction in the Computing Department at Lancaster University. She holds bachelor degrees in Computer Science and Psychology and an M.A. in Industrial Psychology from Romania. She received her Ph.D. degree in Computer Science from University College Dublin in 2004. Her research interests include user modelling, adaptive systems, data mining, spatial cognition, user studies and individual differences. She has published in various journals and international conferences in these areas.

Nikita Schmidt is a Postdoctoral Research Fellow at University College Dublin (UCD). He received his Ph.D. degree from UCD in 2004 and M.Sc. from St-Petersburg State University, Russia in 1994. His research interests include pervasive, ubiquitous and location-aware computing, embedded systems, hardware-close software development and tree-structured data. His work experience is a mix of industry and academia.

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Sas, C., Schmidt, N. A typology of course of motion in simulated environments based on Bézier curve analysis. Knowl Inf Syst 13, 173–196 (2007). https://doi.org/10.1007/s10115-007-0065-7

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