Dynamical system segmentation for information measures in motion

Thomas A. Berrueta, Ana Pervan, Kathleen Fitzsimons, Todd D. Murphey

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Motions carry information about the underlying task being executed. Previous studies on human motion analysis suggest that complex motions may result from the composition of fundamental submovements called movemes. The existence of finite structure in motion motivates information-theoretic approaches to motion analysis and robotic assistance. We define task embodiment as the amount of task information encoded in an agent's motions. By decoding task-specific information embedded in motion, we can use task embodiment to create detailed performance assessments. We extract an alphabet of behaviors comprising a motion without a priori knowledge using a novel algorithm, which we call dynamical system segmentation. For a given task, we specify an optimal agent, and compute an alphabet of behaviors representative of the task. We identify these behaviors in data from agent executions, and compare their relative frequencies against that of the optimal agent using the Kullback-Leibler divergence. We validate this approach using a dataset of human subjects (n=53) performing a dynamic task, and under this measure find that individuals receiving assistance better embody the task. Moreover, we find that task embodiment is a better predictor of assistance than integrated mean-squared error.

Original languageEnglish (US)
Article number8552420
Pages (from-to)169-176
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number1
StatePublished - Jan 2019

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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