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Conference Paper

Movement extraction by detecting dynamics switches and repetitions

MPS-Authors

Chiappa,  S.
Max Planck Society;

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Peters,  J.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Chiappa, S., & Peters, J. (2011). Movement extraction by detecting dynamics switches and repetitions. In J. Lafferty Williams C.K.I., J. Shawe-Taylor, R. S. Zemel, & A. Culotta (Eds.), 24th Annual Conference on Neural Information Processing Systems (NIPS 2010) (pp. 388-396). Red Hook, NY, USA: Curran.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-4CDF-6
Abstract
Many time-series such as human movement data consist of a sequence of basic actions, e.g., forehands and backhands in tennis. Automatically extracting and characterizing such actions is an important problem for a variety of different applications. In this paper, we present a probabilistic segmentation approach in which an observed time-series is modeled as a concatenation of segments corresponding to different basic actions. Each segment is generated through a noisy transformation of one of a few hidden trajectories representing different types of movement, with possible time re-scaling. We analyze three different approximation methods for dealing with model intractability, and demonstrate how the proposed approach can successfully segment table tennis movements recorded using a robot arm as haptic input device.