@inproceedings{6742,
  title = {Movement extraction by detecting dynamics switches and repetitions},
  journal = {Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010},
  booktitle = {Advances in Neural Information Processing Systems 23},
  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.},
  pages = {388-396},
  editors = {Lafferty, J. , C. K.I. Williams, J. Shawe-Taylor, R. S. Zemel, A. Culotta},
  publisher = {Curran},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Red Hook, NY, USA},
  year = {2010},
  slug = {6742},
  author = {Chiappa, S. and Peters, J.}
}