Renz Ocampo
Papers
8
Total Citations
145
H-Index
6
About
Renz Ocampo is a robotics and human-computer interaction researcher whose work sits at the intersection of rehabilitation engineering, haptic teleoperation, and machine learning. His research focuses on developing intelligent robotic systems that assist patients recovering from physical disabilities and workplace injuries, with a particular emphasis on making therapy more accessible, effective, and engaging. Among his most significant contributions is his work integrating machine learning algorithms into robotic rehabilitation platforms to improve functional capacity evaluation — a paper that has garnered 51 citations since its publication in 2020. Ocampo has pioneered approaches that model a physiotherapist's expertise computationally, enabling robots to replicate clinical guidance in home-based settings through haptic teleoperation (27 citations). His earlier work on bicycle cranking rehabilitation using learning-from-demonstration techniques (22 citations) demonstrated how robots could adapt therapy dynamically to individual patient needs. A recurring theme across Ocampo's portfolio is the fusion of augmented reality with haptic robotics to create immersive, spatially accurate rehabilitation environments, with multiple publications exploring how visual-haptic colocation improves patient performance. His research on occupational rehabilitation further extends these technologies to injured workers re-entering the workforce. Collectively, his body of work reflects a commitment to bridging clinical expertise with cutting-edge robotics to democratize access to high-quality physical rehabilitation.
Research Focus
Key Achievements
Top Papers
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- 8User's task performance in two-handed complementary-motion teleoperation2 citations · 2016