Movement Primitive Segmentation for Human Motion Modeling: A Framework for Analysis

Jonathan Feng Shun Lin, Michelle Karg, Dana Kulić

Research output: Contribution to journalArticleResearchpeer-review

33 Citations (Scopus)

Abstract

Movement primitive segmentation enables long sequences of human movement observation data to be segmented into smaller components, termed movement primitives, to facilitate movement identification, modeling, and learning. It has been applied to exercise monitoring, gesture recognition, human-machine interaction, and robot imitation learning. This paper proposes a segmentation framework to categorize and compare different segmentation algorithms considering segment definitions, data sources, application-specific requirements, algorithm mechanics, and validation techniques. The framework is applied to human motion segmentation methods by grouping them into online, semionline, and offline approaches. Among the online approaches, distance-based methods provide the best performance, while stochastic dynamic models work best in the semionline and offline settings. However, most algorithms to date are tested with small datasets, and algorithm generalization across participants and to movement changes remains largely untested.

Original languageEnglish
Article number7374673
Pages (from-to)325-339
Number of pages15
JournalIEEE Transactions on Human-Machine Systems
Volume46
Issue number3
DOIs
Publication statusPublished - 1 Jun 2016
Externally publishedYes

Keywords

  • Algorithm design and analysis
  • classification algorithms
  • machine learning algorithms
  • physiology
  • time series analysis

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