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Related Concept Videos

Interpreting Run Charts01:25

Interpreting Run Charts

Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...

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Related Experiment Video

Updated: Jun 21, 2026

Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion
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Development of A Knowledge Graph-Based Cause Retrieval System for Post-Stroke Gait Analysis.

Yiran Jiao, Zengkun Liu, Stacey Reading

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
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    Summary
    This summary is machine-generated.

    This study developed a knowledge graph system to identify causes of post-stroke gait deviations, aiding rehabilitation. The system enhances clinical decision-making and workflow for better patient outcomes.

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    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Science
    • Artificial Intelligence

    Background:

    • Stroke frequently causes gait impairments, necessitating thorough gait analysis for effective rehabilitation.
    • Current gait assessment methods may lack efficiency in identifying underlying causes of deviations.

    Purpose of the Study:

    • To develop and evaluate a knowledge graph-based system for automated cause retrieval in post-stroke gait analysis.
    • To enhance the interpretability and clinical utility of AI-driven gait assessment tools.

    Main Methods:

    • Construction of an event knowledge graph integrating the Rancho Los Amigos framework and expert clinical knowledge.
    • Development of an automated cause retrieval system utilizing the knowledge graph.
    • Preliminary evaluation through a case study and user experience assessment using the Technology Acceptance Model (TAM).

    Main Results:

    • The knowledge graph effectively represents relationships between gait deviations and their contributors.
    • The system demonstrated improved interpretability and supported clinical workflows in preliminary evaluations.
    • User experience assessment indicated positive acceptance and potential utility in clinical settings.

    Conclusions:

    • Knowledge graphs offer a promising approach to enhance AI-driven gait analysis for post-stroke rehabilitation.
    • The developed system can assist clinicians in identifying gait deviation causes, potentially leading to more targeted rehabilitation strategies.
    • This technology has the potential to improve clinical decision-making and patient care in post-stroke recovery.