  Human posture recognition provides a dynamic field that has produced many
methods. Using fuzzy subsets based data fusion methods to aggregate the results
given by different types of recognition processes is a convenient way to
improve recognition methods. Nevertheless, choosing a defuzzification method to
imple-ment the decision is a crucial point of this approach. The goal of this
paper is to present an approach where the choice of the defuzzification method
is driven by the constraints of the final data user, which are expressed as
limitations on indica-tors like confidence or accuracy. A practical
experimentation illustrating this ap-proach is presented: from a depth camera
sensor, human posture is interpreted and the defuzzification method is selected
in accordance with the constraints of the final information consumer. The paper
illustrates the interest of the approach in a context of postures based human
robot communication.
