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Studying the Neural Basis of Adaptive Locomotor Behavior in Insects
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A Locomotion Mode Recognition Algorithm Using Adaptive Dynamic Movement Primitives.

Huseyin Eken, Francesco Lanotte, Vito Papapicco

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |October 26, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study presents a novel algorithm for robotic prostheses that accurately recognizes user locomotion intent using adaptive Dynamic Movement Primitive models. This advancement enhances prosthetic control by enabling seamless transitions between walking and stair navigation.

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    Area of Science:

    • Robotics
    • Biomechanics
    • Machine Learning

    Background:

    • Effective control of robotic prostheses requires accurate decoding of user's locomotion intent.
    • Existing systems often struggle with seamless transitions between different movement modes.

    Purpose of the Study:

    • To develop and validate a locomotion mode recognition algorithm for robotic prosthesis control.
    • To utilize adaptive Dynamic Movement Primitive models for classifying walking intentions.

    Main Methods:

    • Employed adaptive Dynamic Movement Primitive models as locomotion templates.
    • Utilized foot-ground contact and thigh roll angle data from inertial measurement units.
    • Extracted features using Support Vector Machines for classification.

    Main Results:

    • Achieved high classification accuracy for initiatory, steady-state, and transitory steps in intact subjects and an amputee.
    • Demonstrated median accuracies up to 100% for steady-state steps in intact subjects.
    • Showcased robust performance across ground-level walking and stair negotiation tasks.

    Conclusions:

    • The proposed algorithm shows significant feasibility for real-time robotic prosthesis control.
    • Adaptive Dynamic Movement Primitives offer a promising approach for intent recognition in lower-limb prosthetics.
    • Accurate locomotion mode recognition is crucial for intuitive and adaptive prosthetic function.