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Movement extraction by detecting dynamics switches and repetitions

Silvia Chiappa; Jan Peters
In: John D. Lafferty; Christopher K. I. Williams; John Shawe-Taylor; Richard S. Zemel; Aron Culotta (Hrsg.). Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Neural Information Processing Systems (NeurIPS-2010), December 6-9, Vancouver, British Columbia, Canada, Pages 388-396, Curran Associates, Inc. 2010.


Many time-series such as human movement data consist of a sequence of basic actions, eg, 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.

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