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    This study introduces a new method for motion assist robots to accurately predict human lower-limb movement intention and its phase in real-time. This enhances robot assistance by understanding user intent and motion stage, even if the user changes their mind.

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    Area of Science:

    • Robotics
    • Human-Computer Interaction
    • Biomechanics

    Background:

    • Motion assist robots aid individuals with mobility impairments.
    • Effective assistance requires real-time estimation of user motion intention.
    • Current methods may lack accuracy in dynamic or changing scenarios.

    Purpose of the Study:

    • To develop a real-time method for estimating human lower-limb motion intention and phase.
    • To improve the effectiveness and responsiveness of motion assist robots.
    • To enable robots to adapt assistance mid-motion.

    Main Methods:

    • Utilized integrated human physical, biological, and environmental data.
    • Employed artificial neural networks with damping neurons for intention and phase estimation.
    • Incorporated changing velocity analysis for early intention detection.

    Main Results:

    • Successfully estimated lower-limb motion intention and phase in real-time.
    • Demonstrated accurate phase estimation even when users abandoned intended motions.
    • Validated the effectiveness of the proposed integrated data and neural network approach.

    Conclusions:

    • The proposed method enhances real-time motion intention and phase estimation for motion assist robots.
    • Accurate phase detection allows for more nuanced and timely robotic assistance.
    • This approach improves the adaptability and user experience of assistive robotic technologies.