A method for developing biomechanical response corridors based on principal component analysis.

Published

Journal Article

The standard method for specifying target responses for human surrogates, such as crash test dummies and human computational models, involves developing a corridor based on the distribution of a set of empirical mechanical responses. These responses are commonly normalized to account for the effects of subject body shape, size, and mass on impact response. Limitations of this method arise from the normalization techniques, which are based on the assumptions that human geometry linearly scales with size and in some cases, on simple mechanical models. To address these limitations, a new method was developed for corridor generation that applies principal component (PC) analysis to align response histories. Rather than use normalization techniques to account for the effects of subject size on impact response, linear regression models are used to model the relationship between PC features and subject characteristics. Corridors are generated using Monte Carlo simulation based on estimated distributions of PC features for each PC. This method is applied to pelvis impact force data from a recent series of lateral impact tests to develop corridor bounds for a group of signals associated with a particular subject size. Comparing to the two most common methods for response normalization, the corridors generated by the new method are narrower and better retain the features in signals that are related to subject size and body shape.

Full Text

Cited Authors

  • Sun, W; Jin, JH; Reed, MP; Gayzik, FS; Danelson, KA; Bass, CR; Zhang, JY; Rupp, JD

Published Date

  • October 2016

Published In

Volume / Issue

  • 49 / 14

Start / End Page

  • 3208 - 3215

PubMed ID

  • 27553847

Pubmed Central ID

  • 27553847

Electronic International Standard Serial Number (EISSN)

  • 1873-2380

International Standard Serial Number (ISSN)

  • 0021-9290

Digital Object Identifier (DOI)

  • 10.1016/j.jbiomech.2016.07.034

Language

  • eng