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Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging
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Resolution Influence on 3D Anthropometric Data Clustering for Fitting Design.

Jianwei Niu1, Zhizhong Li, Gavriel Salvendy

  • 1School of Mechanical Engineering, University of Science and Technology, Beijing 100083, China.

Industrial Health
|October 17, 2009
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Summary
This summary is machine-generated.

Wavelet analysis combined with K-means clustering effectively reduces computational load for 3D anthropometric data. Clustering at the third decomposition level maintains over 95% accuracy for face and head scans.

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Anthropometry

Background:

  • 3D anthropometric data improves wearable product fit but poses computational challenges.
  • Wavelet analysis was previously used to create multi-resolution descriptions of 3D data, reducing complexity.
  • K-means clustering was applied to these decomposed 3D samples.

Purpose of the Study:

  • To investigate the impact of wavelet decomposition levels on K-means clustering results for 3D anthropometric data.
  • To determine the optimal decomposition level for balancing computational efficiency and clustering accuracy.
  • To validate findings using face, head, and upper head scan datasets.

Main Methods:

  • Applied wavelet analysis to decompose 3D anthropometric data (face, head, upper head samples).
  • Performed K-means clustering on data at five different decomposition levels.
  • Evaluated clustering consistency using the Cluster Membership Accuracy Rate (CMAR) against original data clustering.

Main Results:

  • CMAR values demonstrated decreasing clustering accuracy with increased decomposition levels.
  • For face data, CMAR ranged from 100% (level 0) to 93.39% (level 4).
  • For head and upper head data, CMAR at level 3 consistently exceeded 95% accuracy.

Conclusions:

  • Clustering 3D anthropometric data at the third decomposition level offers a suitable balance between reduced computational load and maintained accuracy.
  • This approach is effective for face and head scans, ensuring at least 95% clustering accuracy.
  • Optimizing decomposition levels is crucial for efficient processing of large 3D datasets.