Multiple correspondence analysis of biomechanical signals characterized through fuzzy histograms

Abstract

Data characterizing is considered the first and main stage of the statistical analysis. Rather than characterizing each biomechanical signal through one or few global indicators, such as the mean or the root mean square, this paper suggests first to cut the scale into several fuzzy windows and to summarize the data within each window through an occurrence indicator. These indicators become the analysis variables. They can be analyzed through the multiple correspondence analysis, which shows the most discriminant variables, connections between them, empirical situation classes and correspondences between these classes and the most discriminant variables. An example is considered for arguing our point of view; it concerns characterizing and analysis of forces situated at the hand, foot and back level in a load lifting task.