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Comparing multiple correspondence and principal component analyses with biomechanical signals. Example with turning the steering wheel

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  • P. Loslever
  • J. Schiro
  • F. Gabrielli
  • P. Pudlo

Abstract

The purpose of this article is to compare Principal Component Analysis (PCA) and a much less used method, i.e. MCA (Multiple Correspondence Analysis) with data being first changed into membership values to fuzzy space windows. For such a comparison, data from an experimental study about turning the steering wheel is used. In a didactic perspective, this article only considers one multidimensional signal with 5 components: 3 linked to the steering wheel angle and hand positions and 2 to hand effort variables. A discussion weighs out the pros and the cons of both methods with criteria such as the possibility to show complex relational phenomena, the analysis/computing time or the information loss inherent to the averaging stage (in the perspective to analyze several hundreds of large multidimensional signals).

Suggested Citation

  • P. Loslever & J. Schiro & F. Gabrielli & P. Pudlo, 2017. "Comparing multiple correspondence and principal component analyses with biomechanical signals. Example with turning the steering wheel," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 20(10), pages 1038-1047, July.
    • Handle: RePEc:taf:gcmbxx:v:20:y:2017:i:10:p:1038-1047
    DOI: 10.1080/10255842.2017.1331341
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    References listed on IDEAS

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    1. Maureen Stone & Xiaofeng Liu & Hegang Chen & Jerry L. Prince, 2010. "A preliminary application of principal components and cluster analysis to internal tongue deformation patterns," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 13(4), pages 493-503.
    2. F. Gabrielli & J. Schiro & P. Pudlo & A. Thevenon, 2013. "Hand orientation while steering," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 16(S1), pages 41-43, July.
    3. Lianne Jones & Cathy A. Holt & Malcolm J. Beynon, 2008. "Reduction, classification and ranking of motion analysis data: an application to osteoarthritic and normal knee function data," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 11(1), pages 31-40.
    4. J. Schiro & F. Gabrielli & P. Pudlo & F. Barbier & M. Djemai, 2013. "Comparison of steering forces of healthy versus disabled drivers under simulation condition," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 16(S1), pages 118-120, July.
    5. A. Van Hamme & W. Samson & B. Dohin & R. Dumas & L. Chèze, 2013. "Is there a predominant influence between heel height, upper height and sole stiffness on young children gait dynamics?," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 16(S1), pages 66-67, July.
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