Modeling movement primitives with Hidden Markov models for robotic and biomedical applications

Michelle Karg, Dana Kulić

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Otherpeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationHidden Markov Models
Subtitle of host publicationMethods and Protocols
EditorsDavid R. Westhead, M. S. Vijayabaskar
Place of PublicationNew York NY USA
PublisherHumana Press
Pages199-213
Number of pages15
ISBN (Electronic)9781493967537
ISBN (Print)9781493967513
DOIs
Publication statusPublished - 2017
Externally publishedYes

Publication series

NameMethods in Molecular Biology
Volume1552
ISSN (Print)1064-3745

Keywords

  • Gait analysis
  • Hidden Markov models
  • Movement analysis
  • Rehabilitation
  • Robotics

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