Yinhua Liu

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About

Yinhua Liu is a researcher at the forefront of human–machine interaction and rehabilitation engineering, with a primary focus on decoding motor intent from surface electromyography (sEMG) signals. Their most-cited work, "Continuous Gesture Recognition and Force Estimation Using sEMG Signal" (2023, 9 citations), addresses a critical challenge in the field: moving beyond discrete gesture classification to enable simultaneous, continuous recognition of hand gestures and estimation of applied force. This dual-task approach is vital for intuitive control of prosthetic limbs and robotic exoskeletons, where both the type of movement and the amount of force matter. By integrating signal processing and machine learning, Liu’s research bridges the gap between raw physiological data and practical, real-time control systems. Their contributions are particularly impactful for advancing assistive technologies that restore function for individuals with motor impairments. As the field pushes toward more natural and responsive interfaces, Liu’s work on continuous sEMG decoding stands as a key step in making prosthetic and robotic systems feel like a seamless extension of the human body.