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Related Concept Videos

Ankle Joint01:10

Ankle Joint

1.9K
The ankle is formed by the talocrural joint (crural = leg). It consists of the articulations between the talus bone of the foot and the distal ends of the tibia and fibula of the leg. The superior aspect of the talus bone is square-shaped and has three areas of articulation. The top of the talus articulates with the inferior tibia. This is the portion of the ankle joint that carries the body weight between the leg and foot. The sides of the talus are firmly held in position by the articulations...
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Related Experiment Video

Updated: Sep 16, 2025

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis
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Tailoring Ankle-Foot Orthoses Stiffness to End-Users' Needs: Which Performance Variables Matter?

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    IEEE ... International Conference on Rehabilitation Robotics : [Proceedings]
    |July 11, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Optimizing Ankle Foot Orthoses (AFOs) stiffness requires a personalized approach. This study shows that key gait variables differ between users, highlighting the need for individualized AFO settings for better outcomes.

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    Area of Science:

    • Biomechanics
    • Rehabilitation Engineering
    • Clinical Gait Analysis

    Background:

    • Ankle Foot Orthoses (AFOs) are crucial for gait assistance and rehabilitation.
    • Current AFO stiffness optimization lacks standardized clinical guidelines, leading to variable user outcomes and satisfaction.
    • Existing methods often fail to capture gait complexity due to limited variables and user-specific responses.

    Purpose of the Study:

    • To develop and evaluate a user-tailored framework for optimizing AFO stiffness.
    • To identify and incorporate individual gait variables crucial for personalized AFO settings.
    • To explore the impact of personalized AFO stiffness on gait performance in individuals with cerebral palsy (CP).

    Main Methods:

    • A pilot study involving two participants with cerebral palsy (CP).
    • Analysis of key gait performance variables using machine learning models (XGBoost) and explainability techniques (SHAP values).
    • Focus on identifying user-specific relationships between AFO stiffness and gait characteristics.

    Main Results:

    • The study identified multiple performance variables essential for capturing the complexity of gait.
    • Crucially, the most relevant gait variables for AFO optimization varied significantly between the two participants.
    • This demonstrates the inherent individuality in how AFO stiffness affects gait.

    Conclusions:

    • A personalized approach to AFO stiffness optimization is essential for improving user outcomes.
    • The proposed user-tailored framework shows promise in addressing the limitations of current methods.
    • Further research is warranted to validate these findings in larger, diverse populations.