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Efficient and Robust Skeleton-Based Quality Assessment and Abnormality Detection in Human Action Performance.

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    This study uses low-cost depth sensing to detect neuromusculoskeletal disorders in elderly individuals through motion analysis. Vision-based methods accurately assess motion quality and detect abnormalities for safer, independent living.

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    Area of Science:

    • Biomedical Engineering
    • Computer Vision
    • Gerontology

    Background:

    • Early detection of neuromusculoskeletal disorders is crucial for elderly independence.
    • Low-cost depth sensing offers unobtrusive motion analysis for healthcare.
    • Assessing motion quality and detecting abnormalities can enhance elder care.

    Purpose of the Study:

    • To develop and evaluate vision-based methods for detecting neuromusculoskeletal disorders.
    • To assess the quality of motion in elderly individuals using 3D skeletal data.
    • To enable safer and more independent living for the elderly through automated health monitoring.

    Main Methods:

    • Utilized 3D skeletal data from depth cameras (e.g., MS Kinect).
    • Extracted medically-justified features to create motion descriptors.
    • Trained a probabilistic normalcy model for abnormality detection.
    • Employed linear regression for quantitative motion quality assessment.

    Main Results:

    • The proposed methods demonstrated promising results in detecting abnormalities and assessing motion quality.
    • Evaluated on daily actions: sit-to-stand, stand-to-sit, walking, and stair gait.
    • Validated on both public and in-clinic datasets including patients with neuromusculoskeletal disorders.

    Conclusions:

    • Vision-based motion analysis using depth sensing is effective for detecting neuromusculoskeletal disorders.
    • This technology is a step towards convenient in-home automated healthcare services for the elderly.
    • Improved motion analysis can significantly contribute to elder safety and independence.