Temporal segmentation and recognition of body motion data based on inter-limb correlation analysis

Abstract

A method for segmentation and recognition of human body behavior data is proposed. Recognition of human body movements is getting larger interests in robotic research field, since robots must recognize human behavior in order to interact with human in the real world. In addition, there is demand for quantitative methods to analyze human body movements, since human body movements can be used as models of robot behaviors. The author proposes a scheme for human behavior recognition based on two process steps: analysis of movement correlations among limbs and temporal segmentation of motion data. Inter-limb movement correlations are widely observed in various behaviors and well represent contents of behavior, so it will be a universal feature value for general behavior. Observing changes of inter-limb correlations, we can segment motion capture data into temporal fragment of action units. Using this segmentation technique in an experiment, the system succeeded recognizing various types of human behavior efficiently.