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An EMG-Based Objective Function for Human-in-the-Loop Optimization.

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    Researchers developed a new method using electromyography (EMG) to estimate human energetic expenditure during walking. This approach offers a simpler alternative to traditional energetic cost (EC) measurements for optimizing wearable robots.

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    Area of Science:

    • Biomechanics
    • Robotics
    • Human-Computer Interaction

    Background:

    • Wearable robots aim to enhance user performance and comfort by optimizing energetic cost (EC).
    • Traditional EC measurements are physiologically delayed, noisy, require long evaluation periods, and necessitate uncomfortable equipment like masks.
    • Existing methods for optimizing human-robot interaction often rely on these cumbersome EC measurements.

    Purpose of the Study:

    • To develop and validate an electromyography (EMG)-based objective function to estimate and minimize the natural energetic expenditure during walking.
    • To establish a simpler, more repeatable method for assessing energetic demands compared to traditional EC measurements.
    • To explore the potential of this EMG-based function for optimizing wearable robot performance and human-robot interaction.

    Main Methods:

    • Combined multiple electromyography (EMG) variables, including EMG intensity and muscle synergies, to create an objective function for energetic expenditure.
    • Prescribed step frequency (SF) using a metronome to simplify evaluation and optimize this frequency to minimize muscle activity demands.
    • Utilized a linear mixed-effects model to relate EMG variables to EC, followed by a cubic polynomial to identify the optimal SF that minimizes the EMG-based objective function.

    Main Results:

    • The proposed EMG-based objective function demonstrated comparability to traditional energetic cost (EC) measurements during walking.
    • Optimizing step frequency based on the EMG objective function effectively reduced muscle activity demands.
    • The study successfully identified an optimal step frequency that minimizes the developed EMG-based objective function.

    Conclusions:

    • The developed EMG-based objective function provides a viable and simpler alternative for estimating and minimizing energetic expenditure during human locomotion.
    • This EMG-based approach has the potential to significantly improve the efficiency and comfort of human-robot interaction in wearable robotics.
    • The findings pave the way for more effective, non-invasive optimization strategies in the field of wearable assistive devices.