David Oliva Uribe

Papers

Total Citations

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About

David Oliva Uribe is a researcher advancing the field of surgical robotics through innovative tactile sensing technologies. His work focuses on developing sensor systems that enhance the precision and safety of robotic-assisted surgery, particularly by enabling real-time tissue differentiation. Uribe’s most-cited paper, “Improved tactile resonance sensor for robotic assisted surgery” (2017, 34 citations), introduces a refined tactile resonance sensor that provides surgeons with critical haptic feedback during minimally invasive procedures. This contribution addresses a key limitation in robotic surgery—the loss of tactile sensation—by allowing instruments to detect subtle variations in tissue stiffness. Building on this, his 2018 study “Tactile sensor-based real-time clustering for tissue differentiation” (4 citations) explores computational methods to classify tissue types in real time, using sensor data to distinguish between healthy and pathological tissues. Though early in its citation impact, this work signals a promising direction for autonomous surgical decision-making. Uribe’s research sits at the intersection of mechatronics, sensor design, and clinical application, offering practical solutions that could improve surgical outcomes. His contributions are particularly relevant for engineers and clinicians seeking to integrate haptic intelligence into next-generation surgical platforms.