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Estimating infant upper extremities motion with an RGB-D camera and markerless deep neural network tracking: A

D Balta, H H Kuo, J Wang

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
    Summary

    This study introduces a low-cost, markerless motion tracking method for infant upper body movement analysis. This technique aids in identifying biomarkers for predicting movement disorders in infants.

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    Area of Science:

    • Biomedical Engineering
    • Developmental Pediatrics
    • Computer Vision

    Background:

    • Quantitative biomarkers of infant motion are crucial for predicting movement disorders.
    • Current methods for motion tracking can be costly or complex.
    • Markerless motion analysis offers a potential low-cost alternative.

    Purpose of the Study:

    • To present and validate a low-cost, markerless motion tracking method for estimating infant upper body kinematics.
    • To extract quantitative biomarkers from infant motion for movement disorder prediction.
    • To assess the accuracy of the proposed method using a validated setup.

    Main Methods:

    • Utilized a single RGB-D camera and the DeepLabCut 2D motion tracking software.
    • Developed an algorithm to generate 3D coordinates from 2D points, handling missing data.
    • Validated the method with a doll on a rotating turntable, testing two camera orientations.

    Main Results:

    • Evaluated upper body segment lengths, joint angles, and linear point velocity against known values.
    • Determined Mean Absolute Errors (MAE) for each kinematic measure, indicating expected margins of error.
    • The 45° camera angle simulation highlighted challenges with occlusions, relevant to live infant recordings.

    Conclusions:

    • The validated markerless motion tracking method provides a feasible approach for obtaining quantitative biomarkers from infant upper body motion.
    • The identified MAE offers a benchmark for expected accuracy in clinical applications.
    • Future improvements in hardware and tracking software can further reduce error margins for enhanced predictive capabilities in movement disorders.