Ellena, Thierry and Subic, Aleksandar and Mustafaa, Helmy and Yen, Pang Toh (2018) A novel hierarchical clustering algorithm for the analysis of 3D anthropometric data of the human head. Computer-Aided Design & Applications, 5 (1). pp. 25-33. ISSN 1686-4360
Full text not available from this repository. (Request a copy)Abstract
In recent years, the use of 3D anthropometry for product design has become more appealing because of advances in mesh parameterisation, multivariate analyses and clustering algorithms. The purpose of this study was to introduce a new method for the clustering of 3D head scans. A novel hierarchical algorithm was developed, in which a squared Euclidean metric was used to assess the head shape similarity of participants. A linkage criterion based on the centroid distance was implemented, while clusters were created one after another in an enhanced manner. As a result, 95.0% of the studied sample was classified inside one of the four computed clusters. Compared to conventional hierarchical techniques, our method could classify a higher ratio of individuals into a smaller number of clusters, while still satisfying the same variation requirements within each cluster. The proposed method can provide meaningful information about the head shape variation within a population, and should encourage ergonomists to use 3D anthropometric data during the design process of head and facial gear.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 3D anthropometric data; clustering algorithm; hierarchical algorithm |
| Subjects: | Q Science > QA Mathematics > QA299.6-433 Analysis |
| Divisions: | Faculty of Technology Management and Business > Department of Business Management |
| Depositing User: | UiTM Student Praktikal |
| Date Deposited: | 02 Dec 2021 03:32 |
| Last Modified: | 02 Dec 2021 03:32 |
| URI: | http://eprints.uthm.edu.my/id/eprint/4325 |
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