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Related Experiment Video

Updated: Jan 12, 2026

Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients
05:23

Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients

Published on: March 11, 2021

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A knowledge graph-based post-stroke gait assessment system: A pilot study.

Yiran Jiao1, Zengkun Liu2, Stacey Reading1

  • 1Department of Exercise Sciences, Faculty of Science, The University of Auckland, Auckland, New Zealand.

Medical Engineering & Physics
|November 1, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces an AI-powered system using knowledge graphs for automatic post-stroke gait analysis, improving clinical utility. The system accurately identifies gait issues, aiding clinicians in patient assessment and treatment.

Area of Science:

  • Biomechanics
  • Artificial Intelligence
  • Clinical Assessment

Background:

  • Instrumented gait analysis (IGA) is underutilized in clinical practice due to complex data interpretation requirements.
  • Existing AI-based gait assessment systems lack sufficient clinical utility for post-stroke patients.
  • There is a need for improved, clinically applicable tools to enhance post-stroke gait assessment.

Purpose of the Study:

  • To develop a clinically oriented automatic post-stroke gait assessment system utilizing a knowledge graph (KG).
  • To integrate artificial intelligence with IGA for improved identification of gait abnormalities and their causes.
  • To enhance the clinical utility and interpretation of IGA data for healthcare professionals.

Main Methods:

  • Construction of a domain-specific knowledge graph (KG) for gait analysis.
Keywords:
Automatic systemDecision makingGait analysisGait rehabilitationKnowledge graphStroke

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Last Updated: Jan 12, 2026

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  • Development of an AI system to process IGA data, identifying gait abnormalities and causes via kinematic analysis and KG.
  • Preliminary evaluation involving twenty post-stroke patients and four domain experts.

    • Main Results:

    • The system demonstrated high performance with an average recall of 1, precision of 0.78, and F-score of 0.89.
    • Clinical professionals showed a strong intention to use the system (4.33 ± 0.41 on a 5-point Likert scale).
    • The system effectively identified gait abnormalities and potential causes in post-stroke patients.

    Conclusions:

    • The developed KG-based system shows significant potential for clinical application in post-stroke gait assessment.
    • This system can provide valuable supplementary insights, enhancing the interpretation and clinical utility of IGA.
    • The KG schema is adaptable for gait analysis in other neurological conditions.