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    Summary
    This summary is machine-generated.

    This study introduces a Selective Linear-Regression Model for simultaneous hand posture and grip force control in myoelectric hands. The new method significantly improves accuracy by analyzing electromyography (EMG) signal features specific to each posture.

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    Area of Science:

    • Biomedical Engineering
    • Robotics
    • Signal Processing

    Background:

    • Myoelectric hands often struggle to simultaneously control hand posture and grip force due to complex electromyography (EMG) signal interactions.
    • Existing systems typically offer either posture control or force estimation, limiting functional capabilities.

    Purpose of the Study:

    • To develop and validate a novel method for simultaneous hand posture discrimination and grip force estimation in myoelectric hands.
    • To overcome the limitations of current myoelectric control systems by achieving accurate dual-functionality.

    Main Methods:

    • Utilized a Selective Linear-Regression Model approach.
    • Measured and analyzed EMG signals during varying hand postures and grip forces.
    • Developed regression models correlating specific EMG features with grip force for each distinct posture.

    Main Results:

    • Demonstrated that EMG features increase monotonically with grip force, with distinct patterns for different postures.
    • Achieved significant improvements in both hand posture discrimination accuracy and grip force estimation precision.
    • Reduced failure rates and estimation errors compared to conventional methods.

    Conclusions:

    • The proposed Selective Linear-Regression Model effectively enables simultaneous hand posture discrimination and grip force estimation.
    • This advancement offers enhanced control and functionality for myoelectric prosthetic devices.
    • The findings pave the way for more intuitive and capable human-machine interfaces in prosthetics.