Dr. Cheng Shen – Biomimetic robotics – Best Researcher Award
Shenyang Aerospace University | China
Profiles
📍Current Position
He is a distinguished researcher and engineer currently affiliated with Shenyang Aerospace University. He is deeply involved in advancing the field of biomimetic robotics, leveraging his expertise to develop innovative solutions inspired by biological systems.
📝Publication Achievements
He has made significant contributions to the academic community, evidenced by his numerous publications in top-tier journals and conferences. Notable works include: “Toward generalization of sEMG-based pattern recognition: a novel feature extraction for gesture recognition” published in IEEE Transactions on Instrumentation and Measurement (2022). “STMI: stiffness estimation method based on sEMG-driven model for elbow joint” in IEEE Transactions on Instrumentation and Measurement (2023). “Lower limb activity recognition based on sEMG using stacked weighted random forest” in IEEE Transactions on Neural Systems & Rehabilitation Engineering (2023). “A wearable knee rehabilitation system based on graphene textile composite sensor: implementation and validation” in Engineering Applications of Artificial Intelligence (2024). “Size control of single-crystal perovskite nanoplatelets based on vapor deposition” in Optical Materials (2020).
🔍Ongoing Research
He is actively engaged in several key research projects: Active Rehabilitation Exoskeleton Robot Based on Brain-Controlled CPG: A project under the Key Research and Development Program of Zhejiang Province, focusing on developing a brain-controlled central pattern generator (CPG) for rehabilitation robots. sEMG-based Swing Frequency Gait Recognition Algorithm: Aiming to enhance rehabilitation control systems by integrating sEMG signals with CPG. Human-Machine Collaborative Control for Bench-Type Lower Limb Exoskeleton Rehabilitation Robots: A National Natural Science Foundation of China-funded project focusing on discrete motion intention recognition and collaborative control mechanisms.
🔬Research Interests
His research focuses on several cutting-edge areas: Biological Signal Processing: Exploring techniques for analyzing and interpreting biological signals. Pattern Recognition: Developing algorithms for identifying patterns in complex data. Biomimetic Robotics: Creating robotic systems that mimic biological entities. Exoskeleton Robots: Designing wearable robots for rehabilitation and assistance.
🎓Academic Background
His academic journey is marked by a commitment to advanced manufacturing and mechanical engineering. He is set to complete his Ph.D. in Advanced Manufacturing at Beihang University in July 2024, under the supervision of Prof. Zhongcai Pei and Prof. Weihai Chen. He previously earned an M.S. in Mechanical Engineering in 2019 and a B.S. in Mechatronic Engineering in 2016, both from Liaoning Technical University.
🏆Scholarships and Awards
Throughout his academic career, Chen Shen has received various scholarships and accolades, recognizing his contributions to advanced manufacturing and robotics. These awards have supported his research endeavors and provided platforms for international collaboration.
🌐Professional Associations
He is a member of several professional associations, including IEEE and the Chinese Association for Artificial Intelligence. He has received numerous awards, recognizing his contributions to robotics and engineering.
📚Training & Workshops
He has participated in various training programs and workshops, enhancing his skills in deep learning, algorithm development, and intellectual property management.
🎤Oral Presentations
He has presented his research at prestigious conferences, including the IECON Annual Conference of the IEEE Industrial Electronics Society and the IEEE Conference on Industrial Electronics and Applications, sharing his insights on sEMG signal processing and exoskeleton robots.
🧑🔬Tasks Completed as a Researcher
Developed deep learning algorithms for autonomous driving applications. Established a patent pool and conducted technological research in optoelectronics. Explored laser cladding process parameters for industrial applications.
🚀Success Factors
His success can be attributed to his strong academic foundation, innovative research approach, and dedication to advancing biomimetic robotics. His ability to translate complex biological concepts into practical robotic systems sets him apart in the field.
🧪Publications & Laboratory Experience
He has a prolific publication record, with articles published in esteemed journals and conference proceedings. His laboratory experience spans diverse areas, including robotics, bioinformatics, and material science, where he has developed novel systems and algorithms to solve real-world problems.