A stochastic framework for movement strategy identification and analysis

Muhammad U. Choudry, Tyson A.C. Beach, Jack P. Callaghan, Dana Kulic

Research output: Contribution to journalArticleResearchpeer-review

12 Citations (Scopus)


The human body has many biomechanical degrees of freedom, and thus, multiple movement strategies can be employed to execute a given task. Joint loading patterns and risk of injury are highly sensitive to themovement strategy employed. This paper develops a computational framework to automatically identify and recognize different movement strategies to perform a task from human motion data. A divisive clustering approach is developed to identify movement strategies. Hidden Markov models (HMMs) are trained with the clustered observation sequences to generate strategy-specific models that are improved iteratively by using the maximum likelihood to relocate sequences to the most suitable cluster. Differences in individual joint trajectories are compared across strategies using a stochastic distance measure. The proposed algorithm is compared against three existing algorithms-joint contribution vector, decision tree, and HMM-based agglomerative clustering. Experimental results indicate that the proposed approach performs better than existing algorithms to detect motion strategies and automatically determine the differences between the strategies.

Original languageEnglish
Article number6502256
Pages (from-to)314-327
Number of pages14
JournalIEEE Transactions on Human-Machine Systems
Issue number3
Publication statusPublished - 1 May 2013
Externally publishedYes


  • Clustering
  • Human motion analysis
  • Stochastic models

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