Exploitation of Multidimensional Data Movement, from Different Space-Time Summary Levels: A Loadlifting Case Study

At the end of an experiment or observation design which uses a system based on cameras, markers and computers (and perhaps other measurement devices), one has to cope with a very large and complex data base. This data base has both a multivariate aspect (with tens of kinematic and kinetic variables) and a multifactor aspect (with individual and time factors, at the very least). Given these two aspects, this study investigates motion data by first using an exploratory multivariate and multifactor approach with two analyses: 1 / space windowing without any time windowing; 2 / space windowing with time windowing introduced. In both cases, the membership values are studied using Multiple Correspondence Analysis (MCA). To show this point of view, we consider an experimental study aimed at comparing two lifting modes: isokinetic and free. The experimental design considers 13 individuals with repeated measures: for each mode, three levels are considered (three speeds and three loads). For each of the 78 experimental situations, a signal with 232 components is computed from the measurement of markers