Skip to Main content Skip to Navigation
Journal articles

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

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).
Document type :
Journal articles
Complete list of metadata

https://hal-uphf.archives-ouvertes.fr/hal-03429992
Contributor : Kathleen Torck Connect in order to contact the contributor
Submitted on : Tuesday, November 16, 2021 - 8:34:08 AM
Last modification on : Wednesday, November 17, 2021 - 4:01:18 AM

Identifiers

Collections

Citation

Pierre Loslever, Jessica Schiro, François Gabrielli, Philippe Pudlo. 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, 2017, 20 (10), pp.1038-1047. ⟨10.1080/10255842.2017.1331341⟩. ⟨hal-03429992⟩

Share

Metrics

Record views

5