A novel hierarchical clustering algorithm for the analysis of 3D anthropometric data of the human head
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 a...
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| Format: | Article |
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Taylor and Francis Inc.
2018
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| Online Access: | http://eprints.uthm.edu.my/4325/ https://doi.org/10.1080/16864360.2017.1353727 |
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| _version_ | 1833417569900429312 |
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| author | Ellena, Thierry Subic, Aleksandar Mustafaa, Helmy Yen, Pang Toh |
| author_facet | Ellena, Thierry Subic, Aleksandar Mustafaa, Helmy Yen, Pang Toh |
| author_sort | Ellena, Thierry |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Article |
| id | my.uthm.eprints-4325 |
| institution | Universiti Tun Hussein Onn Malaysia |
| publishDate | 2018 |
| publisher | Taylor and Francis Inc. |
| record_format | eprints |
| spelling | my.uthm.eprints-43252021-12-02T03:32:31Z http://eprints.uthm.edu.my/4325/ A novel hierarchical clustering algorithm for the analysis of 3D anthropometric data of the human head Ellena, Thierry Subic, Aleksandar Mustafaa, Helmy Yen, Pang Toh QA299.6-433 Analysis 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. Taylor and Francis Inc. 2018 Article PeerReviewed 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 https://doi.org/10.1080/16864360.2017.1353727 |
| spellingShingle | QA299.6-433 Analysis Ellena, Thierry Subic, Aleksandar Mustafaa, Helmy Yen, Pang Toh A novel hierarchical clustering algorithm for the analysis of 3D anthropometric data of the human head |
| title | A novel hierarchical clustering algorithm for the analysis of 3D anthropometric data of the human head |
| title_full | A novel hierarchical clustering algorithm for the analysis of 3D anthropometric data of the human head |
| title_fullStr | A novel hierarchical clustering algorithm for the analysis of 3D anthropometric data of the human head |
| title_full_unstemmed | A novel hierarchical clustering algorithm for the analysis of 3D anthropometric data of the human head |
| title_short | A novel hierarchical clustering algorithm for the analysis of 3D anthropometric data of the human head |
| title_sort | novel hierarchical clustering algorithm for the analysis of 3d anthropometric data of the human head |
| topic | QA299.6-433 Analysis |
| url | http://eprints.uthm.edu.my/4325/ https://doi.org/10.1080/16864360.2017.1353727 |
| url_provider | http://eprints.uthm.edu.my/ |