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Motion Capture Data Analysis in the Instantaneous Frequency-Domain Using Hilbert-Huang Transform.

Ran Dong1, Dongsheng Cai2, Soichiro Ikuno1

  • 1School of Computer Science, Tokyo University of Technology, Tokyo 192-0982, Japan.

Sensors (Basel, Switzerland)
|November 19, 2020
PubMed
Summary

This study introduces a new framework using Hilbert-Huang Transform (HHT) to analyze human motion in the frequency domain. This method decomposes complex motions into distinct primitives for better understanding of movement characteristics.

Keywords:
Hilbert spectral analysisHilbert-Huang transformempirical mode decompositionfeature extractionmotion analysismotion capture datamotion primitive

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Area of Science:

  • Biomechanics
  • Signal Processing
  • Data Analysis

Background:

  • Motion capture data is crucial across medical, entertainment, and industry fields.
  • Current motion research predominantly uses time-domain analysis, limiting a full understanding of complex human movements.
  • Frequency-domain analysis is essential for a comprehensive understanding of human motion dynamics.

Purpose of the Study:

  • To present a novel framework for transforming motion capture data into the instantaneous frequency domain.
  • To utilize the Hilbert-Huang Transform (HHT) for analyzing complex human motions.
  • To reveal distinct motion primitives within complex movements.

Main Methods:

  • The framework employs the Hilbert-Huang Transform (HHT), incorporating Empirical Mode Decomposition (EMD).
  • Multivariate EMD is used to decompose nonstationary and nonlinear motion signals into intrinsic mode functions (IMFs).
  • The Hilbert spectrum is analyzed to extract and visualize motion characteristics in the instantaneous frequency domain.

Main Results:

  • Human motions can be decomposed into a finite set of nonlinear modes (IMFs) representing distinct motion primitives.
  • The framework successfully analyzes and visualizes motion characteristics in the instantaneous frequency domain.
  • Applied to jump, injured gait, and golf swing motions, demonstrating versatility.

Conclusions:

  • The proposed HHT-based framework effectively analyzes human motion in the instantaneous frequency domain.
  • Decomposition into IMFs aids in identifying and understanding fundamental motion primitives.
  • This approach offers a novel perspective for analyzing complex human movements across various applications.