Jennifer K. Leestma
Papers
4
Total Citations
23
H-Index
3
About
Jennifer K. Leestma is a pioneering researcher at the intersection of biomechanics, robotics, and machine learning, whose work focuses on enhancing human balance and locomotion through wearable robotic systems. Her primary research areas include exoskeleton design, bipedal locomotion control, and perturbation-based balance modulation. Leestma’s major contributions are exemplified by her development of the "Dynamic Duo," an autonomous hip exoskeleton that actively modulates balance during perturbed locomotion, and her groundbreaking integration of signal temporal logic (STL) into model predictive control for robust bipedal walking—the first study of its kind. She has also advanced fall prevention through a data-driven approach that estimates the human center of mass state using simulated wearable sensors, and a machine learning framework that enables rapid slip detection via a robotic hip exoskeleton. With over 20 citations across her most-cited works, Leestma’s research is already shaping assistive technology for vulnerable populations, including industry workers and older adults. Her innovative fusion of control theory and AI-driven detection marks her as a rising leader in rehabilitation robotics and human-robot interaction.
Research Focus
Key Achievements
Top Papers
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