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    Summary
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    Context-informed incremental learning (CIIL) improves myoelectric control by adapting to users in real-time. A novel zero-shot adaptation (ZS-A) approach achieved superior online performance compared to traditional screen-guided training (SGT).

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    Area of Science:

    • Biomedical Engineering
    • Human-Computer Interaction
    • Robotics

    Background:

    • Traditional myoelectric interfaces rely on screen-guided training (SGT), which inadequately reflects real-world user interactions.
    • Adaptive learning frameworks are crucial for enhancing the intuitiveness and performance of human-machine interfaces.

    Purpose of the Study:

    • To evaluate a user-in-the-loop context-informed incremental learning (CIIL) framework for myoelectric control.
    • To compare CIIL's zero-shot adaptation (ZS-A) and SGT-based adaptation (SGT-A) against a standard SGT baseline.
    • To introduce and assess an adaptive sigmoid-based proportional control mapping for improved user control.

    Main Methods:

    • Sixteen participants performed a Fitts' Law targeting task using SGT, SGT-A, and ZS-A control schemes.
    • Performance was quantified using online throughput and offline classification accuracy.
    • A novel adaptive sigmoid-based proportional control mapping was implemented to refine control signal dynamics.

    Main Results:

    • The ZS-A CIIL approach yielded the highest online throughput (1.47 ± 0.46 bits/s), surpassing the SGT baseline (1.15 ± 0.37 bits/s).
    • ZS-A achieved competitive performance within 200 seconds, despite lower offline accuracy.
    • The adaptive sigmoid mapping enhanced control precision and responsiveness, aligning better with natural user movements.

    Conclusions:

    • CIIL frameworks, particularly ZS-A, demonstrate superior online performance over traditional SGT for myoelectric interfaces.
    • Real-time, user-in-the-loop data is vital for developing adaptable and intuitive myoelectric control systems.
    • Findings have significant implications for advancing prosthetics, rehabilitation devices, and telerobotic systems.