Design and Implementation of a Nonverbal Information Analysis System

抄録

In recent years, achieving sustainable development goals has required of personalized support systems that can adapt to diverse users through natural human–system interactions. Understanding the nonverbal information of users is essential for facilitating these interactions. In this study, we focus on human gestures as a fundamental nonverbal modality that conveys user emotions and intentions. Therefore, we propose a gesture analysis system based on the relative positions of human joints and arm orientations. Using an RGB-D camera, we acquire human skeletal information and develop a gesture recognition system. Within this system, two analytical approaches are investigated: dynamic time warping-based classification method and a neural network-based classification methods. Through experimental evaluation of a small-scale dataset, we analyze the behavioral characteristics and recognition tendencies of each approach under controlled conditions. In addition, we present several cases demonstrating the effectiveness of the proposed system and discuss its applicability.