Papers
6
Total Citations
164
H-Index
3
About
Jenifer Miehlbradt is a leading researcher at the intersection of rehabilitation robotics, human-machine interfaces, and neural control of movement. Her work focuses on developing intuitive, data-driven technologies to restore and enhance motor function after neurological injury. She pioneered a body–machine interface that translates subtle upper body movements into accurate drone control (91 citations), demonstrating how simple, natural signals can replace complex joystick-based systems. In spinal cord injury research, she showed that closed-loop control of trunk posture can regulate leg proprioceptive feedback to improve locomotion (40 citations), revealing a critical link between trunk stability and gait recovery. Miehlbradt is also advancing personalized robot-aided therapy, using statistical models to adapt exoskeleton training to individual patient needs (25 citations). Her pilot studies on motor improvement estimation and resting-state functional connectivity after stroke lay groundwork for tailoring rehabilitation to each patient’s neural state. By merging biomechanics, machine learning, and clinical neuroscience, Miehlbradt is creating smarter, more accessible assistive technologies that empower individuals to regain independence and control over their movements.
Research Focus
Key Achievements
Top Papers
- 1Data-driven body–machine interface for the accurate control of drones91 citations · 2018
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