Movement Primitive Segmentation for Human Motion Modeling: A Framework for Analysis - Lin, J. F. S.; Karg, M. E.; Kulić, D. (IEEE Transactions on Human-Machine Systems, 2016)
Movement primitive segmentation enables long sequences of human movement observation data to be segmented into smaller components, termed movement primitives, to facilitate movement identification, modeling, and learning. It has been applied to exercise monitoring, gesture recognition, human-machine interaction, and robot imitation learning. This paper proposes a segmentation framework to categorize and compare different segmentation algorithms considering segment definitions, data sources, application-specific requirements, algorithm mechanics, and validation techniques. The framework is applied to human motion segmentation methods by grouping them into online, semionline, and offline approaches. Among the online approaches, distance-based methods provide the best performance, while stochastic dynamic models work best in the semionline and offline settings. However, most algorithms to date are tested with small datasets, and algorithm generalization across participants and to movement changes remains largely untested.
Keywords: Algorithm design and analysis; algorithm mechanics; application-specific requirements; Cameras; classification algorithms; Data collection; data sources; Databases; distance-based methods; exercise monitoring; gesture recognition; human motion modeling; human movement observation data sequences; human-machine interaction; image motion analysis; image segmentation; machine learning algorithms; Manuals; Motion segmentation; movement identification; movement learning; movement modeling; movement primitive segmentation; physiology; robot imitation learning; segment definitions; segmentation algorithms; stochastic dynamic models; time series analysis; validation techniques