Manufacturing & Industrial Engineering Faculty Publications
Document Type
Article
Publication Date
8-25-2025
Abstract
Gracia de Luna conducted experiments with an HMD virtual environment in which human subjects were presented with surprise distractions. His collected data for head, dominant hand, and non-dominant hand included 6 DOF human subject trajectories. This paper examines this data from 57 human subject responses to those surprise virtual environment distractions using statistical trajectory clustering algorithms. The data is organized and processed with a Dynamic Time Warping (DTW) algorithm and then analyzed using the Density Based Spatial Clustering (DBSCAN) algorithm. The K-means method was used to determine the appropriate number of clusters. Chi Squared goodness of fit was used to determine statistical significance. For five of the data sets, a p value of less than 0.05 was found. These five data sets were found to have a limited relationship to the measured variables.
Recommended Citation
Avila, Martin Galicia, Douglas Timmer, and Alley Butler. 2025. “Using Statistical Clustering of Trajectory Data to Support Analysis of Subject Movement in a Virtual Environment.” Proceedings of the Design Society 5 (August): 3361–70. https://doi.org/10.1017/pds.2025.10350.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Publication Title
Proceedings of the Design Society
DOI
10.1017/pds.2025.10350
Comments
© The Author(s) 2025 This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.