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Correcting whole-body motion capture data using rigid body transformation.

Makoto Miyakoshi1

  • 1Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, California, USA.

The European Journal of Neuroscience
|November 20, 2021
PubMed
Summary

This study introduces rigid body transformation for motion capture data in mobile brain-body imaging. This method effectively recovers missing markers and denoises data, improving neurocognitive experiment preprocessing.

Keywords:
Mobile brain-body imagingaudiomazemissing dataspline interpolation

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Area of Science:

  • Neuroscience
  • Biomechanical Engineering
  • Data Science

Background:

  • Mobile brain-body imaging (MoBI) increasingly utilizes motion capture data.
  • Preprocessing motion capture data is crucial for accurate neurocognitive experiments.
  • Existing methods may lack efficiency or require extensive linear algebra knowledge.

Purpose of the Study:

  • To present rigid body transformation as a preprocessing step for motion capture data in MoBI.
  • To demonstrate its utility in denoising and recovering missing markers.
  • To provide an accessible method with minimal linear algebra requirements.

Main Methods:

  • Rigid body transformation applied to motion capture data.
  • Simulation tests using empirical datasets (AudioMaze project).
  • Application and analysis of the transformation on real MoBI data.

Main Results:

  • Rigid body transformation accurately recovers missing markers (simulations).
  • Preprocessing corrected data shows 90-95% marker recovery for rigid body parts.
  • Identified need for across-trial corrections (42% within-participant, 11% across-participants).

Conclusions:

  • Rigid body transformation is an intuitive and powerful correction method for MoBI.
  • This technique is essential for preprocessing motion capture data in neurocognitive research.
  • The method offers necessary-and-sufficient solutions with minimal assumptions.