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Head-and-face shape variations of U.S. civilian workers

机译:美国文职人员的头脸形状变化

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The objective of this study was to quantify head-and-face shape variations of U.S. civilian workers using modern methods of shape analysis. The purpose of this study was based on previously highlighted changes in U.S. civilian worker head-and-face shape over the last few decades - touting the need for new and better fitting respirators - as well as the study's usefulness in designing more effective personal protective equipment (PPE) - specifically in the field of respirator design. The raw scan three-dimensional (3D) data for 1169 subjects were parameterized using geometry processing techniques. This process allowed the individual scans to be put in correspondence with each other in such a way that statistical shape analysis could be performed on a dense set of 3D points. This process also cleaned up the original scan data such that the noise was reduced and holes were filled in. The next step, statistical analysis of the variability of the head-and-face shape in the 3D database, was conducted using Principal Component Analysis (PCA) techniques. Through these analyses, it was shown that the space of the head-and-face shape was spanned by a small number of basis vectors. Less than 50 components explained more than 90% of the variability. Furthermore, the main mode of variations could be visualized through animating the shape changes along the PCA axes with computer software in executable form for Windows XP. The results from this study in turn could feed back into respirator design to achieve safer, more efficient product style and sizing. Future study is needed to determine the overall utility of the point cloud-based approach for the quantification of facial morphology variation and its relationship to respirator performance.
机译:这项研究的目的是使用现代形状分析方法来量化美国文职人员的头部和面部形状变化。这项研究的目的是基于过去几十年来美国平民工作者头部和头部形状的变化(吹捧对新型更合适的呼吸器的需求),以及该研究在设计更有效的个人防护设备方面的有用性(PPE)-特别是在呼吸器设计领域。使用几何处理技术对1169个对象的原始扫描三维(3D)数据进行了参数化。此过程使各个扫描彼此对应,从而可以对密集的3D点集执行统计形状分析。此过程还清理了原始扫描数据,从而降低了噪声并填补了孔洞。下一步,使用主成分分析(3D)对头部和面部形状的变异性进行统计分析( PCA)技术。通过这些分析,表明头形形状的空间被少量的基矢量所覆盖。少于50个成分说明了超过90%的可变性。此外,可以通过使用Windows XP可执行格式的计算机软件对PCA轴上的形状变化进行动画处理,来显示变化的主要模式。这项研究的结果又可以反馈到呼吸器设计中,以实现更安全,更有效的产品样式和尺寸。需要进一步的研究来确定基于点云的方法对面部形态变化及其与呼吸器性能之间关系的量化的整体效用。

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