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Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Related Experiment Video

Updated: Oct 10, 2025

Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality
08:45

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MarkerLess Motion Capture: ML-MoCap, a low-cost modular multi-camera setup.

Jinne E Geelen, Mariana P Branco, Nick F Ramsey

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary

    We developed a markerless motion capture system for 3D human movement analysis. This modular system integrates with machine learning for objective diagnosis and therapy, improving upon marker-based methods.

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    Last Updated: Oct 10, 2025

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    Area of Science:

    • Biomechanics
    • Human Movement Analysis
    • Biomedical Engineering

    Background:

    • Marker-based motion capture systems are standard for analyzing human movement but can be cumbersome and restrict natural motion.
    • Objective diagnosis and effective therapy for motor system conditions rely on accurate human movement tracking.

    Purpose of the Study:

    • To develop and evaluate a novel markerless, modular multi-camera motion capture system for 3D human movement recording.
    • To enable integration with machine learning tools for automated data analysis and biomechanical insights.

    Main Methods:

    • A modular system of interconnected microcomputers and cameras was designed for markerless 3D motion capture.
    • The system synchronizes cameras with sub-millisecond precision and achieves a 40 Hz frame rate.
    • Machine learning tools (DeepLabCut, AniPose) were integrated to convert video to virtual marker trajectories for biomechanical analysis.

    Main Results:

    • The system was evaluated using index finger movement, achieving sub-millisecond synchronization and 40 Hz frame rate.
    • Comparison with a marker-based system showed a root-mean-square error of 7.5 degrees for metacarpophalangeal joint motion.
    • The markerless system demonstrated feasibility for out-of-the-lab studies and potential for full-body tracking.

    Conclusions:

    • The developed markerless, modular motion capture system offers a less intrusive and more versatile alternative to traditional marker-based approaches.
    • Integration with machine learning facilitates automated data labeling and advanced biomechanical analysis.
    • The system's modularity supports diverse applications, from fine motor studies to large-volume, full-body motion capture.