Velocity-Based Multiple Change-Point Inference for Unsupervised Segmentation of Human Movement Behavior

Abstract

In order to transfer complex human behavior to a robot, segmentation methods are needed which are able to detect central movement patterns that can be combined to generate a wide range of behaviors. We propose an algorithm that segments human movements into behavior building blocks in a fully automatic way, called velocity-based Multiple Change-point Inference (vMCI). Based on characteristic bell-shaped velocity patterns that can be found in point-to-point arm movements, the algorithm infers segment borders using Bayesian inference. Different segment lengths and variations in the movement execution can be handled. Moreover, the number of segments the movement is composed of need not be known in advance. Several experiments are performed on synthetic and motion capturing data of human movements to compare vMCI with other techniques for unsupervised segmentation. The results show that vMCI is able to detect segment borders even in noisy data and in demonstrations with smooth transitions between segments.