Ian Sharp
Papers
2
Total Citations
13
H-Index
2
About
Ian Sharp is a researcher at the intersection of rehabilitation robotics and human motor learning, with a focus on developing innovative therapies for stroke survivors and neurologically impaired individuals. His work centers on integrating haptic and graphic feedback systems to enhance motor recovery, particularly through robotic training that reshapes movement patterns. In his highly cited 2011 study, Sharp pioneered the integration of a robot into a virtual environment library for stroke therapy, demonstrating how combining robotic assistance with biofeedback can enable prolonged, engaging practice—a critical factor in neurorehabilitation. This work laid groundwork for interactive systems that adapt to individual patient needs. His 2018 research introduced the concept of "limit-push" robotic training, where artificially induced unstable force fields reshape motion distributions by teaching patients to avoid high-cost movement regions. This approach offers a novel way to retrain motor control in both healthy and impaired populations, with potential applications beyond rehabilitation to sports training and ergonomics. Though his citation counts are modest (8 and 5 respectively), Sharp’s contributions are notable for their creativity and practical relevance, bridging engineering and clinical neuroscience to address real-world therapy challenges.
Research Focus
Key Achievements
Top Papers
- 1
- 2Reshaping Movement Distributions With Limit-Push Robotic Training5 citations · 2018