Catherine Todd
Papers
4
Total Citations
108
H-Index
3
About
Catherine Todd is a leading researcher in the field of neurorehabilitation robotics, with a specific focus on restoring fine hand motor function in chronic stroke patients. Her work lies at the intersection of adaptive control systems, reinforcement learning, and virtual reality, aiming to harness neuroplasticity through highly repetitive, task-oriented training. Todd’s most impactful contribution is her 2017 case study (56 citations), which demonstrated the combined efficacy of adaptive control and virtual reality in a robot-assisted rehabilitation system, showing significant promise for enhancing fine hand motion recovery. She also authored a comprehensive 2016 survey (27 citations) on robot-assisted upper extremity rehabilitation, providing a critical overview of the field’s progress. Her pioneering use of a reinforcement learning neural network (RLNN) for adaptive control (22 citations) represents a key technical advancement, allowing rehabilitation robots to personalize therapy in real time based on patient performance. Through these contributions, Todd has established herself as a key figure in developing intelligent, patient-responsive robotic systems that push the boundaries of post-stroke motor recovery.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4