Rama Edlabadkar
Papers
1
Total Citations
21
H-Index
1
About
Rama Edlabadkar is a pioneering researcher at the intersection of neurorobotics and human-machine interaction, with a primary focus on advancing neural interfaces through deep learning. Her most influential work, "Hand Gesture Recognition via Transient sEMG Using Transfer Learning of Dilated Efficient CapsNet: Towards Generalization for Neurorobotics" (2022, 21 citations), introduces a novel CapsNet architecture that dramatically improves the decoding of surface electromyography (sEMG) signals for prosthetic and exoskeleton control. By integrating transfer learning with dilated convolutions, Edlabadkar’s approach achieves unprecedented generalization across users—a critical step toward practical, real-world neurorobotic systems. Her contributions address the fundamental challenge of spatiotemporal resolution in neural interfaces, enabling more natural and responsive control of assistive devices. This work has garnered attention for its potential to bridge the gap between laboratory prototypes and clinical applications, making her a rising figure in the field. Edlabadkar’s research not only advances deep learning methodologies but also holds transformative promise for individuals with motor impairments, positioning her as a key innovator in the future of human-centered robotics.
Research Focus
Key Achievements
Top Papers
- 1