Decision method choice in a human posture recognition context

TL;DR

This paper presents a method for selecting defuzzification techniques in human posture recognition based on user constraints, demonstrated through experiments with depth camera data for improved human-robot communication.

Contribution

It introduces a user-driven approach for choosing defuzzification methods in fuzzy data fusion for posture recognition, tailored to application-specific constraints.

Findings

Effective selection of defuzzification methods improves recognition accuracy.

User constraints can guide the choice of data fusion parameters.

Application in human-robot interaction enhances communication reliability.

Abstract

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.