Comparing multiple correspondence and principal component analyses with biomechanical signals. Example with turning the steering wheel. Issue 10 (27th July 2017)

• Record Type: Journal Article
• Authors: Loslever, P.; Schiro, J.; Gabrielli, F.; Pudlo, P.
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 20:Issue 10(2017)
• Page Start: 1038
• Page End: 1047
• Publication Date: 2017-07-27
• Subjects: Kinematics -- efforts -- fuzzy segmentation -- principal component analysis, multiple correspondence analysis -- turning the steering wheel; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2017.1331341
• Languages: English
• ISSNs: 1025-5842

Abstract

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).