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

P. Loslever, J. Schiro, F. Gabrielli and P. Pudlo

Computer Methods in Biomechanics and Biomedical Engineering, 2017, vol. 20, issue 10, 1038-1047

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).

Date: 2017
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DOI: 10.1080/10255842.2017.1331341

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