Shabnam Sadeghi Esfahlani
Papers
4
Total Citations
148
H-Index
3
About
Shabnam Sadeghi Esfahlani is a researcher at the intersection of robotics, computer vision, and intelligent systems, with a particular focus on autonomous unmanned aerial vehicles (UAVs), rehabilitation technology, and deep learning-based navigation. Her most influential contribution, cited 95 times, pioneered a mixed reality framework integrating inertial measurement units, consumer-grade cameras, and fire detection algorithms within nano UAV platforms — a significant advancement in aerial inspection and emergency response systems. Building on this foundation, her earlier work on fire detection using ROS and computer vision laid the groundwork for these applied UAV systems. Beyond aerial robotics, Esfahlani has made meaningful contributions to healthcare technology through ReHabGame, a serious game platform combining adaptive fuzzy logic control with rehabilitation robotics to support patients with neuromuscular disorders, garnering 27 citations. Her more recent work on deep convolutional neural networks for autonomous mobile robot navigation (24 citations) demonstrates her expanding expertise in AI-driven spatial reasoning and landmark recognition. Collectively, her research reflects a sustained commitment to deploying intelligent, adaptive systems across diverse real-world challenges, from disaster response to patient rehabilitation.
Research Focus
Key Achievements
Top Papers
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