Leonardo Jaramillo
Papers
1
Total Citations
8
H-Index
1
About
Leonardo Jaramillo’s research lies at the intersection of biomedical engineering and artificial intelligence, with a primary focus on developing intelligent systems for human-machine interaction. His most-cited work, “A Neural Network embedded system for real-time identification of EMG signals” (2018, 8 citations), introduces a novel embedded artificial neural network (ANN) designed to decode electromyography (EMG) signal patterns in real time. This system serves as a critical interface between users and robotic upper-limb prostheses, enabling more intuitive and responsive control. Jaramillo’s contribution is significant: by embedding the ANN directly into the hardware, he addresses the latency and computational constraints that often hinder practical prosthetic applications. His methodology demonstrates how machine learning can be deployed on resource-limited devices, bridging the gap between algorithm development and real-world assistive technology. While his citation count is modest, the work’s applied nature—targeting a tangible improvement in prosthetic functionality—marks him as a researcher focused on translational impact. For students and researchers, Jaramillo’s approach exemplifies how neural networks can move beyond simulation to empower individuals with disabilities, highlighting the transformative potential of embedded AI in biomedical systems.
Research Focus
Key Achievements
Top Papers
- 1