Shape Analysis of 3D Head Scan Data for U.S. Respirator Users

<p/> <p>In 2003, the National Institute for Occupational Safety and Health (NIOSH) conducted a head-and-face anthropometric survey of diverse, civilian respirator users. Of the 3,997 subjects measured using traditional anthropometric techniques, surface scans and 26 three-dimensional (3D...

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Main Authors: Slice DennisE, Benson Stacey, Zhuang Ziqing, Lynch Stephanie, Viscusi DennisJ
Format: Article
Language:English
Published: SpringerOpen 2010-01-01
Series:EURASIP Journal on Advances in Signal Processing
Online Access:http://asp.eurasipjournals.com/content/2010/248954
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spelling doaj-9442ed0c5745474ea37432e8f04c61dc2020-11-24T22:50:02ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802010-01-0120101248954Shape Analysis of 3D Head Scan Data for U.S. Respirator UsersSlice DennisEBenson StaceyZhuang ZiqingLynch StephanieViscusi DennisJ<p/> <p>In 2003, the National Institute for Occupational Safety and Health (NIOSH) conducted a head-and-face anthropometric survey of diverse, civilian respirator users. Of the 3,997 subjects measured using traditional anthropometric techniques, surface scans and 26 three-dimensional (3D) landmark locations were collected for 947 subjects. The objective of this study was to report the size and shape variation of the survey participants using the 3D data. Generalized Procrustes Analysis (GPA) was conducted to standardize configurations of landmarks associated with individuals into a common coordinate system. The superimposed coordinates for each individual were used as commensurate variables that describe individual shape and were analyzed using Principal Component Analysis (PCA) to identify population variation. The first four principal components (PC) account for 49% of the total sample variation. The first PC indicates that overall size is an important component of facial variability. The second PC accounts for long and narrow or short and wide faces. Longer narrow orbits versus shorter wider orbits can be described by PC3, and PC4 represents variation in the degree of ortho/prognathism. Geometric Morphometrics provides a detailed and interpretable assessment of morphological variation that may be useful in assessing respirators and devising new test and certification standards. </p>http://asp.eurasipjournals.com/content/2010/248954
collection DOAJ
language English
format Article
sources DOAJ
author Slice DennisE
Benson Stacey
Zhuang Ziqing
Lynch Stephanie
Viscusi DennisJ
spellingShingle Slice DennisE
Benson Stacey
Zhuang Ziqing
Lynch Stephanie
Viscusi DennisJ
Shape Analysis of 3D Head Scan Data for U.S. Respirator Users
EURASIP Journal on Advances in Signal Processing
author_facet Slice DennisE
Benson Stacey
Zhuang Ziqing
Lynch Stephanie
Viscusi DennisJ
author_sort Slice DennisE
title Shape Analysis of 3D Head Scan Data for U.S. Respirator Users
title_short Shape Analysis of 3D Head Scan Data for U.S. Respirator Users
title_full Shape Analysis of 3D Head Scan Data for U.S. Respirator Users
title_fullStr Shape Analysis of 3D Head Scan Data for U.S. Respirator Users
title_full_unstemmed Shape Analysis of 3D Head Scan Data for U.S. Respirator Users
title_sort shape analysis of 3d head scan data for u.s. respirator users
publisher SpringerOpen
series EURASIP Journal on Advances in Signal Processing
issn 1687-6172
1687-6180
publishDate 2010-01-01
description <p/> <p>In 2003, the National Institute for Occupational Safety and Health (NIOSH) conducted a head-and-face anthropometric survey of diverse, civilian respirator users. Of the 3,997 subjects measured using traditional anthropometric techniques, surface scans and 26 three-dimensional (3D) landmark locations were collected for 947 subjects. The objective of this study was to report the size and shape variation of the survey participants using the 3D data. Generalized Procrustes Analysis (GPA) was conducted to standardize configurations of landmarks associated with individuals into a common coordinate system. The superimposed coordinates for each individual were used as commensurate variables that describe individual shape and were analyzed using Principal Component Analysis (PCA) to identify population variation. The first four principal components (PC) account for 49% of the total sample variation. The first PC indicates that overall size is an important component of facial variability. The second PC accounts for long and narrow or short and wide faces. Longer narrow orbits versus shorter wider orbits can be described by PC3, and PC4 represents variation in the degree of ortho/prognathism. Geometric Morphometrics provides a detailed and interpretable assessment of morphological variation that may be useful in assessing respirators and devising new test and certification standards. </p>
url http://asp.eurasipjournals.com/content/2010/248954
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