Daniel Bonilla
Papers
1
Total Citations
15
H-Index
1
About
Daniel Bonilla is a researcher at the forefront of rehabilitation robotics, with a focus on restoring motor function for stroke survivors. His work integrates electromyography (EMG)-based gesture classification with model predictive control (MPC)-driven exoskeletons to create progressive rehabilitation systems. His most-cited paper, "Progressive Rehabilitation Based on EMG Gesture Classification and an MPC-Driven Exoskeleton" (2023, 15 citations), addresses the critical need for adaptive therapy in the 80% of stroke patients who experience motor disability. By designing a robot that interprets muscle signals and adjusts assistance in real-time, Bonilla’s work bridges the gap between human intent and machine response, offering a personalized path to recovery. His contributions are particularly impactful given the global prevalence of stroke—affecting over 200 million people—and the pressing demand for scalable, intelligent rehabilitation tools. Bonilla’s research not only advances assistive robotics but also sets a foundation for future innovations in neurorehabilitation, making him a promising voice in the field.
Research Focus
Key Achievements
Top Papers
- 1