R. Darin Ellis
Papers
21
Total Citations
613
H-Index
11
About
R. Darin Ellis is a prominent human factors and biomedical engineering researcher whose work sits at the intersection of robotic-assisted surgery, surgical skill assessment, and human-robot interaction. Based at Wayne State University, Ellis has made significant contributions to advancing automation and objective evaluation in minimally invasive surgical environments. His most influential work, "Automated Robot-Assisted Surgical Skill Evaluation: Predictive Analytics Approach" (2017, 184 citations), introduced a groundbreaking framework for replacing subjective surgical assessments with data-driven, machine learning models — a contribution that has resonated broadly across surgical education and robotics communities. Complementing this, his series of papers on gesture segmentation, task recognition, and machine learning for skill evaluation (collectively accumulating over 100 citations) has helped lay the groundwork for smarter, semi-autonomous surgical systems. Ellis has also advanced the understanding of camera automation in laparoscopic surgery and pioneered augmented reality cueing systems to improve teleoperator performance under challenging conditions. His decade-spanning body of work reflects a sustained commitment to making robotic surgery safer, more trainable, and increasingly intelligent — offering both practical tools and theoretical frameworks that continue to shape the field.
Research Focus
Key Achievements
Top Papers
- 1
- 2A Review of Camera Viewpoint Automation in Robotic and Laparoscopic Surgery85 citations · 2014
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- 5Towards an Autonomous Robot for Camera Control During Laparoscopic Surgery43 citations · 2013
- 6Machine Learning Approach for Skill Evaluation in Robotic-Assisted Surgery36 citations · 2016
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- 9Task analysis of laparoscopic camera control schemes20 citations · 2015
- 10