Benzhen Guo

Papers

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About

Benzhen Guo is a leading researcher at the intersection of rehabilitation robotics and deep learning, with a primary focus on myoelectric signal processing for upper-limb assistive technologies. His most cited work, "Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation" (2020, 21 citations), demonstrates a groundbreaking approach to human-robot interaction. Guo pioneered the use of lightweight convolutional neural networks to automatically extract high-level features from raw surface electromyography signals, eliminating the need for manual feature engineering. This innovation enables real-time, intuitive control of robotic arms for patients undergoing upper-limb rehabilitation, significantly improving the responsiveness and naturalness of assistive devices. By proving that deep models can directly decode motion intents from unprocessed myoelectric data, Guo has advanced the practical deployment of intelligent prosthetics and exoskeletons. His work bridges the gap between complex machine learning architectures and real-world clinical applications, offering a scalable solution for personalized rehabilitation. With a growing citation record, Guo continues to shape the future of neural-machine interfaces, making him a key figure in the development of next-generation, patient-centered robotic therapies.