Papers
4
Total Citations
68
H-Index
4
About
Daniel Caballero is an emerging researcher at the intersection of surgical ergonomics, wearable technology, and artificial intelligence, with a focus on improving conditions for surgeons performing minimally invasive procedures. His work centers on the objective measurement and prediction of physiological stress and ergonomic strain in both conventional and robotic-assisted laparoscopic surgery, a field with significant implications for surgeon health and patient safety. Caballero's most impactful contribution, a 2024 comparative study garnering 42 citations, pioneered the use of wearable sensors to capture real-time physiological and ergonomic data during live surgical procedures. Building on this foundation, he has developed and refined artificial intelligence models — particularly artificial neural networks — trained on electrodermal activity (EDA) sensor data to predict surgeon stress with increasing accuracy, work that has collectively attracted over 60 citations across his published research. His ongoing investigations into correlation modeling of ergonomic parameters in robotic-assisted tasks demonstrate a commitment to translating raw sensor data into clinically actionable insights. For students and researchers exploring human factors in surgery or AI-driven health monitoring, Caballero's portfolio represents a compelling and rapidly growing body of work shaping the future of surgical practice.
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