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Related Experiment Video

Updated: Dec 30, 2025

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
06:54

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

14.6K

Unsupervised GEI-Based Gait Disorders Detection From Different Views.

Amr Elkholy, Yasushi Makihara, Walid Gomaa

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
    Summary
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    This study introduces an automatic gait disorder detection system using cameras and Gait Energy Images (GEI). The method accurately identifies abnormal gaits for safer elderly living and in-home healthcare.

    Area of Science:

    • Biomedical Engineering
    • Computer Vision
    • Gerontology

    Background:

    • Gait disorders in the elderly pose significant safety risks.
    • Early detection is crucial for timely intervention and improved quality of life.
    • Current methods may lack the convenience for widespread home-based monitoring.

    Purpose of the Study:

    • To develop an automated system for detecting gait disorders using readily available RGB or RGBD cameras.
    • To leverage Gait Energy Images (GEI) for robust gait feature extraction.
    • To enable convenient, in-home health monitoring for the elderly.

    Main Methods:

    • Utilized Gait Energy Images (GEI) computed from RGB/RGBD camera data.
    • Employed a convolutional autoencoder to learn representative features from GEI.

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    Related Experiment Videos

    Last Updated: Dec 30, 2025

    Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
    06:54

    Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

    Published on: March 4, 2018

    14.6K
    Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
    08:56

    Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

    Published on: November 7, 2014

    14.2K
    Comprehensive Understanding of Inactivity-Induced Gait Alteration in Rodents
    04:37

    Comprehensive Understanding of Inactivity-Induced Gait Alteration in Rodents

    Published on: July 6, 2022

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  • Applied anomaly detection techniques to identify abnormal gait patterns.

    • Main Results:

    • Achieved high accuracy in detecting various gait disorders across different views.
    • Demonstrated the method's generalizability on two public datasets.
    • Validated the effectiveness of GEI and deep learning for gait analysis.

    Conclusions:

    • The proposed automatic gait disorder detection method is accurate and generalizable.
    • It offers a viable solution for in-home health monitoring of the elderly.
    • This technology contributes to safer living environments and convenient healthcare services.