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Pouria Chalangari, Thomas Fevens, Hassan Rivaz

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
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    Deep learning with Convolutional Neural Networks (CNNs) shows promise for estimating lower extremity kinematics during jump tests, aiding in Anterior Cruciate Ligament (ACL) injury prevention. This technology offers a more accessible alternative to traditional motion capture systems.

    Area of Science:

    • Biomechanics
    • Computer Vision
    • Sports Medicine

    Background:

    • Anterior Cruciate Ligament (ACL) injuries pose a significant risk to athletes, necessitating effective injury prevention strategies.
    • Assessing knee flexion angles during drop jump landing tests is crucial for identifying individuals at higher risk of knee injuries.
    • Existing technologies like Motion Capture (MoCap) and Microsoft Kinect, while capable, have limitations in accessibility and ease of use.

    Purpose of the Study:

    • To evaluate the efficacy of deep learning-based 3D human pose estimation for analyzing lower extremity kinematics in injury prevention.
    • To compare the performance of CNN-based pose estimation with the Microsoft Kinect sensor as a ground truth in real-world settings.
    • To explore the potential of advanced algorithms for more accessible and accurate injury risk assessment.

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    Main Methods:

    • Utilized deep Convolutional Neural Networks (CNNs) combined with temporal convolutions for 3D human pose estimation from video sequences.
    • Employed the Microsoft Kinect sensor as the ground truth for validating the accuracy of the CNN-based pose estimation.
    • Analyzed lower extremity kinematics, specifically knee flexion angles, during drop jump landing tests.
    • Verified the consistency between two Kinect sensors to ensure reliable ground truth data.

    Main Results:

    • CNN-based 3D human pose estimation demonstrated convincing qualitative and quantitative performance in estimating lower extremity kinematics.
    • The results indicate that deep learning approaches can provide reliable 3D body keypoint information for injury detection.
    • High margins of consistency were observed between multiple Kinect sensors, supporting their use as a reliable ground truth.
    • The study highlights the potential for CNNs to offer a more accessible and user-friendly method compared to traditional systems.

    Conclusions:

    • CNN-based 3D human pose estimation is a promising tool for analyzing biomechanics relevant to injury prevention, particularly for ACL injuries.
    • Further research and improvements in deep learning algorithms can enhance the accuracy of lower extremity kinematics estimation.
    • This approach offers a viable and potentially more accessible alternative to existing motion analysis systems for clinical and research settings.