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A general descriptor for detecting abnormal action performance from skeletal data.

Amr Elkholy, Mohamed E Hussein, Walid Gomaa

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 25, 2017
    PubMed
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    This study introduces a novel, action-independent descriptor for detecting abnormal motion using skeletal features. The method accurately identifies deviations from normal movement across various activities like walking and stair climbing.

    Area of Science:

    • Biomedical Engineering
    • Computer Vision
    • Human Motion Analysis

    Background:

    • Accurate detection of abnormal human motion is crucial for healthcare applications, including patient monitoring and rehabilitation.
    • Existing methods often require action-specific models, limiting their generalizability.
    • Developing an action-independent approach can enhance the robustness and applicability of motion analysis systems.

    Purpose of the Study:

    • To propose and validate an action-independent feature descriptor for detecting abnormal human motion.
    • To assess the effectiveness of the descriptor across diverse daily activities.
    • To establish a robust system for abnormality detection using skeletal data.

    Main Methods:

    • An action-independent descriptor was developed based on medically-inspired skeletal features.

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  • Gaussian Mixture Models (GMMs) were trained on normal motion data for four activities: sit-to-stand, stand-to-sit, flat-walk, and climbing-stairs.
  • Abnormality was assessed by evaluating the likelihood of test sequences fitting the normal motion GMMs.

    • Main Results:

    • The proposed descriptor demonstrated high accuracy in detecting abnormal motion, with results ranging from 0.97 to 1.0 across the tested actions.
    • The action-independent nature of the descriptor proved effective for various complex human movements.
    • The GMM-based approach successfully distinguished between normal and abnormal motion patterns.

    Conclusions:

    • The developed action-independent descriptor is a promising tool for reliable abnormality detection in human motion.
    • This approach offers a generalized solution for motion analysis, applicable to diverse activities without prior action-specific training.
    • The findings support the potential of this method for real-world applications in healthcare and human-computer interaction.