Papers
3
Total Citations
43
H-Index
3
About
Xuebin Hou is a researcher at the intersection of surgical robotics and human-computer interaction, with a primary focus on developing intuitive, vision-based control systems for medical robots. His major contributions center on deep-learning-driven gaze estimation as a natural, hands-free interface for surgical robot control. Hou’s pioneering work, particularly his 2019 paper “Appearance-Based Gaze Estimator for Natural Interaction Control of Surgical Robots” (25 citations), demonstrates how convolutional neural networks can enable surgeons to control robotic arms simply by looking at target points, eliminating the need for joystick or voice commands. His 2018 follow-up (15 citations) further refined this approach, emphasizing efficiency and cost-effectiveness for practical clinical deployment. Beyond gaze control, Hou has also explored voice command recognition for hysterectomy procedures, using MFCC features and CNNs to control robotic arms. His research addresses a critical bottleneck in surgical robotics—the steep learning curve and cumbersome control interfaces—by proposing more natural, accessible interaction methods. With a growing citation footprint, Hou’s work is laying the groundwork for next-generation surgical systems that respond to surgeons’ eyes and voice, promising to make robotic surgery more intuitive and widely adoptable.
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