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dc.contributorInstitute of Textiles and Clothing-
dc.contributorSchool of Design-
dc.creatorLuximon, A-
dc.creatorLuximon, Y-
dc.creatorChao, H-
dc.subjectSurface antropometryen_US
dc.subjectDigital human modelen_US
dc.subjectRecursive regression equationen_US
dc.titleA human surface prediction model based on linear anthropometryen_US
dc.typeJournal/Magazine Articleen_US
dcterms.abstractBody information is needed in product design, medical, archaeological, forensic and many other disciplines. Therefore, anthropometric studies and databases have been developed. Anthropometric measures are useful to some extent, but due to technological innovations, there is a shift toward surface anatomy. As a result, there is a need to shift from linear anthropometry tables to surface model databases. This study provides a general modelling technique, to convert linear anthropometry to complex surface model using recursive regression equations technique (RRET) and scaling technique. The technique makes use of similarities and differences between people. The similarities or standard shape are represented by averaging, while the differences are captured by using anthropometric measures. In order to build the surface model, some scanned data is needed for generating the standard shape. Using RRET techniques a few anthropometric measures are used to predict more anthropometric measures that are then used to scale the standard shape in order to generate a predicted 3D shape. Results indicate that the prediction model is accurate to few millimeters. This level of error is acceptable in different applications. This technique can be applied to generate 3D shape from anthropometry of external shape as well as internal organs. This model is essential to convert the existing large scale anthropometric databases into surface models. It can be applied to product design, sizing and grading, reconstructive surgery, forensic, anthropology and other fields.-
dcterms.bibliographicCitationInternational journal of hybrid intelligent systems, 2013, v. 6, no. 3&4, p. 213-222-
dcterms.isPartOfInternational journal of hybrid intelligent systems-
dc.description.ros2013-2014 > Academic research: refereed > Publication in refereed journal-
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