Daniela Espin-Ramos
Papers
1
Total Citations
10
H-Index
1
About
Daniela Espin-Ramos is a rising researcher at the intersection of robotics, biomedical engineering, and artificial intelligence, with a primary focus on advancing prosthetic and anthropomorphic robotic hand control. Her most-cited work, "Myo Transformer Signal Classification for an Anthropomorphic Robotic Hand" (2023, 10 citations), introduces the multi-channel bio-signal transformer (MuCBiT)—a novel deep learning architecture that classifies surface electromyography (sEMG) signals with unprecedented accuracy. This contribution addresses a critical bottleneck in myoelectric prosthetics: the need for robust, real-time gesture recognition. By leveraging transformer-based attention mechanisms, Espin-Ramos’s approach enables more natural and intuitive control of robotic hands, moving beyond traditional classifiers to capture complex muscle activation patterns. Her work has quickly garnered attention within the rehabilitation robotics community, laying a foundation for next-generation, user-adaptive prosthetic systems. As an early-career scholar, Espin-Ramos demonstrates a clear talent for translating cutting-edge machine learning into tangible biomedical solutions, promising significant impact on assistive technology and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1Myo Transformer Signal Classification for an Anthropomorphic Robotic Hand10 citations · 2023