Fulai Peng

Papers

Total Citations

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About

Fulai Peng is a researcher focused on advancing human–machine interaction through bioelectrical signal processing, with a particular emphasis on surface electromyography (sEMG) for hand gesture recognition. His work directly supports the development of hand rehabilitation robotics, where accurate gesture classification is critical for responsive prosthetic and therapeutic devices. In his most cited study, Peng demonstrated that applying moving average filtering to sEMG features significantly improves hand gesture recognition accuracy—a practical contribution that enhances real-time control in assistive technologies. While his citation count is currently modest, his research addresses a core challenge in rehabilitation engineering: translating muscle activity into reliable, intuitive commands. Peng’s work stands out for its focus on signal preprocessing techniques that are both computationally efficient and clinically relevant, offering a pathway to more natural and effective robotic interfaces. As the demand for non-invasive, wearable rehabilitation systems grows, Peng’s contributions to sEMG-based gesture recognition provide a foundational step toward smarter, more responsive assistive devices.