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Related Experiment Video

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An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
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Published on: May 26, 2020

Detecting changes in motion characteristics during sports training.

Dana Kulić1, Gentiane Venture, Yoshihiko Nakamura

  • 1Department of Mechano-Informatics, University of Tokyo, Tokyo, Japan. dkulic@ece.uwaterloo.ca

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|December 8, 2009
PubMed
Summary

This study introduces a novel stochastic method to track gradual changes in human movement during sports training. The system uses Factorial Hidden Markov Models to analyze movement patterns and detect subtle shifts over time.

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Area of Science:

  • Biomechanics
  • Sports Science
  • Data Science

Background:

  • Analyzing human movement in sports training is crucial for performance and injury prevention.
  • Existing methods may struggle to capture gradual, subtle changes in complex movement patterns.
  • Automated analysis of long-term movement data presents significant challenges.

Purpose of the Study:

  • To propose a stochastic approach for representing and analyzing gradual changes in human movement during sports training.
  • To develop an automated system for extracting and clustering human movement primitives.
  • To evaluate the system's ability to detect subtle, evolving changes in movement over time.

Main Methods:

  • Human movement primitives are modeled using Factorial Hidden Markov Models (FHMMs).
  • The Kullback-Liebler (KL) distance is employed to compare movement models, quantifying information divergence.
  • An automated segmentation and clustering approach is integrated for autonomous data processing.
  • The system is validated on a 4-month marathon training dataset.

Main Results:

  • The proposed system successfully represents and analyzes gradual changes in human movement.
  • Factorial Hidden Markov Models effectively capture the dynamics of movement primitives.
  • Automated segmentation and clustering enabled efficient extraction and grouping of movement patterns.
  • Experimental results confirmed the system's capability to detect subtle, evolving changes in marathon training data.

Conclusions:

  • The stochastic approach using FHMMs provides a robust framework for analyzing gradual changes in human movement.
  • Automated analysis of movement data can effectively identify performance-related adaptations during training.
  • This methodology holds potential for personalized training feedback and injury risk assessment in sports.