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Related Experiment Video

Updated: May 25, 2026

Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits
06:25

Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Published on: August 12, 2019

Automatic classification of pathological gait patterns using ground reaction forces and machine learning algorithms.

Murad Alaqtash1, Thompson Sarkodie-Gyan, Huiying Yu

  • 1Computer Engineering Department, The University of Texas at El Paso, El Paso, TX 79968, USA. msalaqtash@miners.utep.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
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This study introduces an automated method for classifying gait patterns using 3D ground reaction forces (GRFs). The system accurately distinguishes between healthy individuals and those with cerebral palsy (CP) or multiple sclerosis, achieving 95% accuracy with optimal feature selection.

Area of Science:

  • Biomechanics
  • Medical Engineering
  • Computational Science

Background:

  • Pathological gait patterns present diagnostic challenges.
  • Accurate gait analysis is crucial for diagnosing neurological conditions like cerebral palsy (CP) and multiple sclerosis.
  • Current methods may lack the automation and precision needed for widespread clinical application.

Purpose of the Study:

  • To develop an automated method for classifying pathological gait patterns.
  • To discriminate between healthy gait, cerebral palsy (CP), and multiple sclerosis gait using 3D ground reaction force (GRF) data.
  • To evaluate the effectiveness of different feature extraction and selection algorithms for gait classification.

Main Methods:

  • Acquired 3D ground reaction force (GRF) data from healthy, CP, and multiple sclerosis subjects.

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

Published on: March 4, 2018

Related Experiment Videos

Last Updated: May 25, 2026

Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits
06:25

Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Published on: August 12, 2019

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

  • Employed two feature extraction algorithms: GRF parameters and discrete wavelet transform (DWT).
  • Investigated Nearest Neighbor Classifier (NNC) and Artificial Neural Networks (ANN) for gait classification, evaluating various feature sets and applying feature selection algorithms.

    • Main Results:

    • Achieved an initial leave-one-out (LOO) classification accuracy of 85%.
    • Feature selection based on vertical force (M-shaped value) and ANOVA tests for mediolateral and anteroposterior forces significantly improved classification.
    • An optimal feature set of six features enhanced the classification accuracy to 95%.

    Conclusions:

    • An automated gait classification tool using 3D GRFs has been successfully developed.
    • The proposed method demonstrates high accuracy in distinguishing between healthy and pathological gait patterns.
    • This automated tool has the potential to aid clinicians in diagnosing and identifying gait impairments.