Full-body multi-primitive segmentation using classifiers

Abstract

During human-robot interaction, the robot observes a continuous stream of time-series data capturing the behaviour of the human and any changes in the environment. For applications such as imitation learning, intention and gesture recognition, the time-series data is typically segmented into action or motion primitives, requiring accurate and online temporal segmentation. This paper casts the time-series segmentation problem into a two-class classification problem, labelling each data point as either a segment edge or a within-segment point, and applies several common classifiers to a set of full body motion data. The support vector machine combined with principal component analysis dimensionality reduction were found to perform best, with a classification F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score of 91% when applied to novel exemplars. The proposed approach can also generalize to motions unseen during training, achieving a classification F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score of 83% when applied to novel motions.