Shurong Chen
Papers
2
Total Citations
18
H-Index
2
About
Shurong Chen is a researcher at the forefront of biomechatronics and rehabilitation engineering, with a focused expertise in using surface electromyography (sEMG) to decode human movement. Their work centers on the critical challenge of bridging biological signals with robotic control, particularly for lower-limb applications. Chen’s most significant contribution is the development of advanced models for ankle joint torque prediction, a key component for adaptive functional electrical stimulation (FES) and exoskeleton control. By integrating sEMG signals with angular velocity data, their 2020 study (14 citations) provides a robust framework for quantitative limb rehabilitation assessment, offering a more responsive feedback loop for assistive devices. Furthering this line of inquiry, Chen has also pioneered the use of Cerebellar Model Neural Networks to classify complex ankle movements like eversion and inversion from sEMG data (2019, 4 citations). This work is instrumental in improving the precision of movement recognition for both injury diagnosis and prosthetic control. Through these efforts, Chen is making tangible strides toward more intuitive, biologically-driven rehabilitation technologies that can adapt to a patient’s real-time physiological state.
Research Focus
Key Achievements
Top Papers
- 1
- 2