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Related Concept Videos

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Related Experiment Video

Updated: Apr 3, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

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Human-in-the-Loop Pareto Optimization: Trade-Off Characterization for Assist-as-Needed Training and Performance

Harun Tolasa, Volkan Patoglu

    IEEE Transactions on Haptics
    |April 1, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a human-in-the-loop Pareto optimization approach to balance task difficulty and performance in motor rehabilitation. This method enhances training protocols and user performance evaluation.

    Related Experiment Videos

    Last Updated: Apr 3, 2026

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
    11:54

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

    Published on: May 8, 2021

    5.3K

    Area of Science:

    • Robotics
    • Human-Computer Interaction
    • Rehabilitation Engineering

    Background:

    • A key challenge in motor skill training and rehabilitation is the trade-off between task difficulty and user performance.
    • Accurate characterization of this trade-off is essential for effective training protocol design, user performance evaluation, and the development of adaptive assistance strategies.

    Purpose of the Study:

    • To propose and validate a novel human-in-the-loop (HiL) Pareto optimization framework for characterizing the performance-challenge trade-off in motor learning and rehabilitation tasks.
    • To demonstrate the framework's utility in designing adaptive assistance protocols and enabling fair performance evaluations at both group and individual levels.

    Main Methods:

    • Adaptation of Bayesian multi-criteria optimization for efficient HiL Pareto characterization.
    • Hybrid model integrating quantitative performance metrics with qualitative user feedback for perceived challenge assessment.
    • Application within a manual skill training task incorporating haptic feedback.

    Main Results:

    • Demonstrated the framework's ability to design an assist-as needed (AAN) training protocol and evaluate its efficacy against a baseline.
    • Showcased individual-level progress assessment through pre- and post-training trade-off comparisons, offering insights even for users requiring assistance.
    • Validated the framework for fair cross-user performance comparisons by capturing optimal performance across assistance levels.

    Conclusions:

    • The proposed HiL Pareto optimization framework provides a robust method for characterizing the performance-challenge trade-off in motor rehabilitation.
    • This approach facilitates the design of personalized AAN protocols, enables nuanced evaluation of training effectiveness, and allows for equitable comparisons across users.
    • The framework offers significant potential for advancing human-robot interaction in therapeutic and skill-acquisition contexts.