Jannik Haas
Papers
1
Total Citations
6
H-Index
1
About
Jannik Haas is a researcher at the intersection of robotics and rehabilitation engineering, with a primary focus on developing intelligent control systems for robot-assisted therapy. His work centers on iterative learning control and gravity compensation algorithms that enable upper-arm rehabilitation robots to adapt assistance levels in real time, ensuring patients actively participate in their recovery—a key requirement for stimulating neuroplasticity after neurological injuries like stroke. His most-cited paper, "Iterative Learning Control of Gravity Compensation for Upper-Arm Robot-Assisted Rehabilitation" (2020, 6 citations), introduces a method that allows robots to adjust support based on patient performance, reducing therapist supervision while maximizing therapeutic engagement. This contribution addresses a critical challenge in rehabilitation: balancing automated assistance with patient effort to promote motor recovery. Haas’s research has practical implications for making rehabilitation more accessible and effective, particularly for stroke survivors. While his citation count reflects a growing field, his work demonstrates a clear commitment to translating control theory into tangible clinical tools, positioning him as an emerging voice in adaptive robotic therapy.
Research Focus
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Top Papers
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