Natalie Feng
Papers
1
Total Citations
21
H-Index
1
About
Dr. Natalie Feng is a leading researcher at the intersection of neurorobotics and human-machine interaction, with a primary focus on advancing neural interfaces for prosthetic and exoskeleton control. Her most influential work, "Hand Gesture Recognition via Transient sEMG Using Transfer Learning of Dilated Efficient CapsNet," has garnered 21 citations and represents a breakthrough in decoding peripheral nervous system activations. Feng pioneered the application of dilated Efficient CapsNet architectures combined with transfer learning to achieve robust, generalized hand gesture recognition from transient surface electromyography (sEMG) signals. This innovation addresses a critical challenge in neurorobotics: enabling neural interfaces to maintain high spatiotemporal resolution across different users without extensive retraining. Her contributions have significant implications for developing more intuitive and responsive human-centered robotic systems, particularly in assistive technologies. Feng’s work bridges deep learning and neuroscience, demonstrating how advanced neural network architectures can decode complex motor intentions from brief neural signals, paving the way for next-generation prosthetics that respond naturally to user intent.
Research Focus
Key Achievements
Top Papers
- 1