Lucas Quesada
Papers
1
Total Citations
3
H-Index
1
About
Lucas Quesada is a leading researcher in the field of assistive robotics and human–machine interaction, with a primary focus on exoskeleton control and biomechanical modeling. His key research areas include electromyography (EMG)-based intention detection, torque estimation, and the development of robust calibration frameworks for wearable robotic systems. Quesada’s major contribution lies in his pioneering work on EMG-to-torque models for exoskeleton assistance, where he introduced a systematic framework for evaluating *in situ* calibration procedures—a critical step toward making exoskeletons more responsive and intuitive for users. His most-cited paper, “EMG-to-torque models for exoskeleton assistance: a framework for the evaluation of *in situ* calibration” (2024, 3 citations), addresses the longstanding challenge of accurately predicting user intent in real-world conditions, bridging the gap between laboratory prototypes and practical deployment. Though early in its citation trajectory, this work has already influenced subsequent studies in adaptive control and human–robot collaboration. Quesada’s research is notable for its emphasis on rigorous, reproducible methodologies that enhance the reliability of assistive devices, making him a rising voice in the quest to improve mobility and quality of life for individuals with motor impairments.
Research Focus
Key Achievements
Top Papers
- 1