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Improved Trajectory Reconstruction for Markerless Pose Estimation.

R James Cotton, Anthony Cimorelli, Kunal Shah

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Markerless pose estimation accurately reconstructs human movement for gait analysis. Combining a top-down keypoint detector with implicit function trajectory reconstruction yields precise, smooth, and anatomically plausible results.

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    Area of Science:

    • Biomechanics
    • Computer Vision
    • Rehabilitation Technology

    Background:

    • Markerless pose estimation offers a non-invasive method for human movement analysis, with significant potential for applications like gait analysis in clinical settings.
    • Accurate and efficient movement analysis is crucial for monitoring gait impairments and evaluating interventions.
    • The influence of different algorithmic choices on the accuracy of markerless pose estimation remains underexplored.

    Purpose of the Study:

    • To evaluate the impact of various keypoint detectors and reconstruction algorithms on the accuracy of markerless pose estimation.
    • To identify optimal algorithmic configurations for precise human movement reconstruction, particularly for gait analysis.

    Main Methods:

    • Utilized a multicamera system to acquire synchronized and calibrated data from 53 individuals in a rehabilitation hospital.
    • Tested different combinations of keypoint detectors (e.g., top-down) and trajectory reconstruction algorithms (e.g., implicit functions).
    • Compared estimated gait parameters, such as step width, against a gold-standard GaitRite walkway system.

    Main Results:

    • The combination of a top-down keypoint detector and implicit function-based trajectory reconstruction achieved accurate, smooth, and anatomically plausible human movement trajectories.
    • Step width estimation exhibited a low noise level of only 9mm when compared to the GaitRite walkway.
    • Demonstrated the effectiveness of specific algorithmic choices in enhancing markerless pose estimation accuracy.

    Conclusions:

    • Markerless pose estimation, when employing specific algorithmic strategies, provides a viable and accurate method for quantitative gait analysis.
    • The chosen approach enables frequent and precise characterization of gait impairments, facilitating better patient monitoring and intervention assessment.
    • This study provides valuable insights into optimizing markerless pose estimation for clinical movement analysis applications.