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Related Experiment Video

Updated: Nov 1, 2025

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
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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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Sparse Visual-Inertial Measurement Units Placement for Gait Kinematics Assessment.

Randa Mallat, Vincent Bonnet, Raphael Dumas

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |June 17, 2021
    PubMed
    Summary
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    This study demonstrates accurate estimation of lower-limb joint kinematics and gait performance using affordable Visual Inertial Measurement Units (VIMU) and an Extended Kalman Filter (EKF). The method offers a practical solution for physiotherapy assessments outside the lab.

    Area of Science:

    • Biomechanics
    • Wearable Sensors
    • Gait Analysis

    Background:

    • Accurate lower-limb joint kinematics are crucial for physiotherapy assessments.
    • Traditional motion capture systems are often expensive and lab-bound.
    • Markerless systems offer convenience but face accuracy challenges.

    Purpose of the Study:

    • To investigate the estimation of lower-limb joint kinematics and gait performance indexes using a sparse placement of Visual Inertial Measurement Units (VIMU).
    • To evaluate the efficacy of an Extended Kalman Filter (EKF) incorporating biomechanical constraints for improved accuracy.
    • To assess the feasibility of the method for practical physiotherapy applications.

    Main Methods:

    • Utilized three wireless VIMUs placed on the pelvis and heels.

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    Related Experiment Videos

    Last Updated: Nov 1, 2025

    An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
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  • Employed an Extended Kalman Filter (EKF) with two state-vector formulations (constant acceleration and Fourier series).
  • Compared estimated kinematics and gait parameters against a reference stereophotogrammetric system.

    • Main Results:

    • Achieved an average root mean square difference of 3.5° and a correlation coefficient of 0.70 for joint angles.
    • Estimated stride lengths with a normalized root mean square difference below 2%.
    • Gait Deviation Index (GDI) estimations were consistently close to 100, comparable to stereophotogrammetry.

    Conclusions:

    • The proposed VIMU-based EKF method provides accurate lower-limb joint kinematics and gait performance metrics.
    • The Fourier series formulation of the EKF offers a practical, tuning-free approach.
    • This affordable, easy-to-use system is suitable for clinical gait analysis outside laboratory settings.