Jason Fong
Papers
10
Total Citations
205
H-Index
7
About
Jason Fong’s research lies at the intersection of intelligent robotics, machine learning, and rehabilitation medicine, with a focus on creating robotic systems that can learn and emulate the nuanced behaviors of human therapists. His major contributions include pioneering the use of kinesthetic teaching—where a robot physically learns from a therapist’s hands-on guidance—to develop robots capable of assisting with gait therapy, functional capacity evaluation, and occupational rehabilitation. Notably, his work on a therapist-taught robotic system for foot drop therapy and an admittance-controlled assistant for semi-autonomous breast ultrasound scanning demonstrates how robots can enhance both therapeutic consistency and diagnostic repeatability. With his most-cited paper, “Intelligent Robotics Incorporating Machine Learning Algorithms for Improving Functional Capacity Evaluation and Occupational Rehabilitation,” accumulating over 50 citations, Fong’s impact is evident in the growing adoption of his learning-from-demonstration frameworks. He has also explored augmented-reality displays and haptic teleoperation to extend rehabilitation access, and his semi-autonomous control systems for beating-heart surgery highlight the breadth of his contributions. Fong’s work is shaping a future where robots not only assist but actively learn from clinicians to deliver personalized, high-quality care.
Research Focus
Key Achievements
Top Papers
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- 2Kinesthetic teaching of a therapist's behavior to a rehabilitation robot38 citations · 2018
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- 8Semi-Autonomous Surgical Robot Control for Beating-Heart Surgery4 citations · 2019
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