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Modeling Movement Primitives with Hidden Markov Models for Robotic and Biomedical Applications

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Hidden Markov Models

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1552))

Abstract

Movement primitives are elementary motion units and can be combined sequentially or simultaneously to compose more complex movement sequences. A movement primitive timeseries consist of a sequence of motion phases. This progression through a set of motion phases can be modeled by Hidden Markov Models (HMMs). HMMs are stochastic processes that model time series data as the evolution of a hidden state variable through a discrete set of possible values, where each state value is associated with an observation (emission) probability. Each motion phase is represented by one of the hidden states and the sequential order by their transition probabilities. The observations of the MP-HMM are the sensor measurements of the human movement, for example, motion capture or inertial measurements. The emission probabilities are modeled as Gaussians. In this chapter, the MP-HMM modeling framework is described and applications to motion recognition and motion performance assessment are discussed. The selected applications include parametric MP-HMMs for explicitly modeling variability in movement performance and the comparison of MP-HMMs based on the loglikelihood, the Kullback–Leibler divergence, the extended HMM-based F-statistic, and gait-specific reference-based measures.

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Author information

Authors and Affiliations

  1. Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada, N2L 361

    Michelle Karg & Dana Kulić

  2. Continental, Lindau, Germany

    Michelle Karg

Authors
  1. Michelle Karg
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  2. Dana Kulić
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Corresponding author

Correspondence to Michelle Karg .

Editor information

Editors and Affiliations

  1. University of Leeds School of Molecular and Cellular Biology, Leeds, United Kingdom

    David R. Westhead

  2. University of Leeds School of Cellular and Molecular Biology, Leeds, United Kingdom

    M. S. Vijayabaskar

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Karg, M., Kulić, D. (2017). Modeling Movement Primitives with Hidden Markov Models for Robotic and Biomedical Applications. In: Westhead, D., Vijayabaskar, M. (eds) Hidden Markov Models. Methods in Molecular Biology, vol 1552. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6753-7_15

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  • DOI: https://doi.org/10.1007/978-1-4939-6753-7_15

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6751-3

  • Online ISBN: 978-1-4939-6753-7

  • eBook Packages: Springer Protocols

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