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Updated: Jun 25, 2025

3D Kinematic Gait Analysis for Preclinical Studies in Rodents
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Gait Impairment Analysis Using Silhouette Sinogram Signals and Assisted Knowledge Learning.

Mohammed A Al-Masni1, Eman N Marzban2, Abobakr Khalil Al-Shamiri3

  • 1Department of Artificial Intelligence and Data Science, College of Software & Convergence Technology, Sejong University, Seoul 05006, Republic of Korea.

Bioengineering (Basel, Switzerland)
|May 25, 2024
PubMed
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This summary is machine-generated.

This study introduces an AI system for analyzing gait impairments using silhouette sinograms and 1D CNNs. The method achieves high accuracy in recognizing gait abnormalities from video, offering a quantitative, non-invasive assessment.

Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Neurology

Background:

  • Gait analysis is crucial for diagnosing neurological disorders.
  • Current methods for gait impairment assessment can be invasive or subjective.
  • Automated, quantitative analysis of gait from video is needed.

Purpose of the Study:

  • To develop and validate a novel AI-driven system for automated gait impairment analysis from video.
  • To introduce a new 1D representation of gait dynamics called a silhouette sinogram.
  • To leverage 1D Convolutional Neural Networks (CNNs) for spatiotemporal gait analysis.

Main Methods:

  • Generating silhouette sinograms from video frames to represent gait dynamics.
  • Training a 1D CNN model on consecutive silhouette sinograms to capture spatiotemporal gait information.
Keywords:
assisted knowledge learningdeep learninggait analysisgait disorderssilhouette images

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  • Evaluating the system on the INIT GAIT database using frame-level and subject-level analyses.

    • Main Results:

    • Achieved F1-scores of 100%, 90.62%, and 77.32% for 2, 4, and 6 gait abnormalities at the frame level.
    • Achieved perfect F1-scores of 100%, 100%, and 83.33% for 2, 4, and 6 gait abnormalities at the subject level.
    • Demonstrated superior performance in gait impairment recognition compared to conventional methods.

    Conclusions:

    • The proposed AI system provides a quantitative and non-invasive method for evaluating locomotion.
    • Silhouette sinograms combined with 1D CNNs effectively capture spatiotemporal gait information for accurate diagnosis.
    • This approach holds significant potential for the assessment and diagnosis of gait impairments, especially in neurological disorders.