Diego Espejo
Papers
1
Total Citations
4
H-Index
1
About
Diego Espejo is a researcher at the forefront of surgical robotics and biomedical signal processing, with a core focus on vibro-acoustic sensing for tissue characterisation. His most-cited work, "Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions" (2023), provides a comprehensive analysis of feature extraction from vibro-acoustic signals, aiming to decode tissue behaviour during robot-assisted procedures. This foundational study, which has garnered 4 citations, pioneers the use of clustering algorithms to classify tissue types based on subtle acoustic signatures—a critical step toward enhancing intraoperative feedback and surgical precision. By bridging signal processing with clinical robotics, Espejo’s contributions offer a non-invasive, real-time approach to tissue differentiation, potentially reducing reliance on visual cues alone. His work is particularly notable for its potential to improve safety and autonomy in minimally invasive surgeries, laying groundwork for smarter, context-aware robotic systems. For students and researchers, Espejo’s research exemplifies how advanced sensing and machine learning can transform surgical practice, making interventions more adaptive and data-driven.
Research Focus
Key Achievements
Top Papers
- 1