Lila Iznita Izhar
Papers
5
Total Citations
73
H-Index
5
About
Lila Iznita Izhar is a researcher whose work bridges the critical gap between robotics, autonomous systems, and human-assistive technology. Her key research areas include prosthetic design, computer vision, and deep learning for robotics. She made a significant contribution to accessible healthcare with her highly cited 2020 paper on a "3D Printed Robot Hand Structure Using Four-Bar Linkage Mechanism for Prosthetic Application" (44 citations), which addressed the structural challenges of trans-radial prostheses to create more functional, wearable devices for amputees. Izhar has also advanced autonomous driving technology, notably investigating the "synthetic to real gap" in training datasets (8 citations) and pioneering deep feature extraction methods for visual tracking of industrial robots (5 citations). Her earlier work on sEMG-based joint-torque estimation using swarm techniques (5 citations) laid groundwork for rehabilitation robot controllers. Through these diverse contributions—from improving prosthetic hands to enhancing autonomous vehicle perception—Izhar demonstrates a commitment to creating intelligent systems that directly improve human mobility and machine autonomy.
Research Focus
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