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Dr. Pengfeng Li is a leading researcher in rehabilitation robotics and biomedical signal processing, with a focus on developing intelligent control systems for lower limb rehabilitation. His most influential work, "sEMG-based continuous estimation of joint angles of human legs by using BP neural network" (199 citations), pioneered the use of surface electromyography (sEMG) signals for real-time joint angle estimation, enabling more natural and responsive robotic assistance. Dr. Li made significant contributions to rehabilitation robot control strategies, as demonstrated in his work on impedance control for a 3-DOF lower limb rehabilitation robot (29 citations), where he proposed three distinct training modes—passive, damping-active, and spring-active—tailored for patients with paraplegia or hemiplegia. His research also advanced sEMG feature extraction for upper limb motion recognition using discrete wavelet transform (17 citations), which has applications in prosthetic and rehabilitation robot control. Additionally, Dr. Li developed an adaptive RBF neural network control strategy (5 citations) to enhance the adaptability and safety of rehabilitation robots. With over 250 total citations, his work has significantly impacted the fields of rehabilitation engineering, human-robot interaction, and neural control, providing foundational methods for assistive technologies that improve patient outcomes.