Justin Fong
Papers
14
Total Citations
225
H-Index
8
About
Justin Fong is a leading researcher in the field of neurorehabilitation robotics, with a core focus on developing and evaluating robotic systems for upper-limb motor recovery after neurological injury. His work spans the entire translational pipeline, from fundamental device design to clinical adoption. He is the principal designer of the EMU, a transparent 3D robotic manipulandum that enables natural, real-world object interaction during therapy, a device that has garnered 40 citations for its innovative approach. Fong’s highly cited review on iterative learning control (50 citations) established a formal framework for how robots can use a "practice makes perfect" paradigm to optimize motor skill relearning. He has also made significant contributions to understanding the human-robot interface, investigating how exoskeleton dynamics modulate shoulder muscle and joint function, and exploring the reliability of robotic measurements for clinical assessment. Recognizing that technical efficacy alone is insufficient, Fong has applied the extended Technology Acceptance Model to identify the key factors influencing clinician likelihood to adopt robotics, bridging the critical gap between engineering innovation and real-world clinical practice. His open-source CANopen Robot Controller (CORC) further accelerates the field by providing a standardized software stack for human-robot interaction development.
Research Focus
Key Achievements
Top Papers
- 1Learning control in robot-assisted rehabilitation of motor skills – a review50 citations · 2016
- 2EMU: A transparent 3D robotic manipulandum for upper-limb rehabilitation40 citations · 2017
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