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Automated gap-filling for marker-based biomechanical motion capture data.

Jonathan Camargo1, Aditya Ramanathan1, Noel Csomay-Shanklin2

  • 1George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA.

Computer Methods in Biomechanics and Biomedical Engineering
|July 14, 2020
PubMed
Summary

This study introduces an automated method for filling gaps in marker-based motion capture data using inverse kinematics (IK). The new technique significantly reduces errors and completion time compared to manual processing.

Keywords:
Motion capturebiomechanicsgap-fillinginverse kinematics

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Area of Science:

  • Biomechanics
  • Computer Science
  • Robotics

Background:

  • Marker-based motion capture is crucial for analyzing human and animal movement.
  • Data gaps in motion capture are common and traditionally require time-consuming manual correction.
  • Existing software solutions for gap-filling demand substantial user input.

Purpose of the Study:

  • To develop and evaluate an automated method for filling gaps in marker-based motion capture data.
  • To minimize user intervention in the motion capture data processing workflow.
  • To improve the accuracy and efficiency of motion data gap-filling.

Main Methods:

  • An iterative process utilizing inverse kinematics (IK) to minimize error and close data loops.
  • Development of an automated gap-filling algorithm.
  • Comparison of the automated method against traditional manual gap-filling techniques.
  • Integration with OpenSim for motion analysis.

Main Results:

  • Achieved a 21% reduction in worst-case gap-filling error (p < 0.05).
  • Demonstrated an 80% reduction in completion time (p < 0.01) compared to manual methods.
  • The automated IK-based approach significantly outperforms manual gap-filling.

Conclusions:

  • The proposed automated inverse kinematics method offers a highly effective solution for motion capture data gaps.
  • This approach substantially reduces processing time and improves data accuracy.
  • An open-source repository of the method is provided, facilitating wider adoption and further research.