Ruliang Feng
Papers
1
Total Citations
12
H-Index
1
About
Ruliang Feng is a researcher at the forefront of biomedical signal processing and human-machine interaction, with a primary focus on lower limb movement recognition using surface electromyography (sEMG). His most-cited work, "Integration of multiscale fusion of residual neural network with 2-D gramian angular fields for lower limb movement recognition based on multi-channel sEMG signals" (2024), has garnered 12 citations—a strong early indicator of impact in this rapidly evolving field. Feng’s major contribution lies in pioneering a novel deep learning architecture that fuses multiscale residual neural networks with Gramian Angular Field transformations, effectively converting time-series sEMG data into 2D images for enhanced feature extraction. This approach significantly improves the accuracy and robustness of lower limb movement classification, with direct applications in prosthetics control, rehabilitation robotics, and assistive technologies. By bridging signal processing and advanced neural networks, Feng addresses critical challenges in real-time, non-invasive human motion decoding. His work not only advances fundamental understanding of neuromuscular control but also holds promise for developing more intuitive and responsive prosthetic devices. As an emerging scholar, Feng’s innovative integration of multiscale fusion and image-based representations marks him as a rising contributor to the intersection of biomedical engineering and artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1