Papers
2
Total Citations
18
H-Index
2
About
Wei Le is a researcher advancing human-robot interaction in surgical settings, with a focus on making robotic systems more intuitive and accessible for clinicians. Her work centers on two key areas: vision-based control and voice-activated command systems for surgical robots. In her most-cited paper (2018, 15 citations), Le developed an efficient, low-cost deep-learning gaze estimator that enables surgeons to control robotic instruments through eye movement, addressing the steep learning curve associated with traditional surgical robot operation. This work demonstrates her commitment to reducing cognitive load and improving ergonomics in the operating room. She further extended this research by exploring voice control for robotic arms in hysterectomy procedures (2019, 3 citations), using MFCC feature extraction and convolutional neural networks to accurately recognize surgical commands. By integrating multiple modalities—gaze and voice—Le is contributing to more natural, hands-free interfaces that could transform how surgeons interact with robotic assistants. Her research sits at the intersection of computer vision, deep learning, and medical robotics, offering practical solutions for safer, more efficient minimally invasive surgery.
Research Focus
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Top Papers
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