Yancai Xu

Papers

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About

Yancai Xu is a leading researcher in human-robot interaction (HRI), with a primary focus on advancing finger gesture recognition through multimodal data fusion. Their most cited work, "A 3D-CLDNN Based Multiple Data Fusion Framework for Finger Gesture Recognition in Human-Robot Interaction" (2022, 8 citations), addresses a critical bottleneck in the field: the time-consuming labeling and collection of large datasets required for machine learning-based gesture recognition. By proposing a novel 3D convolutional long short-term deep neural network (3D-CLDNN) framework that fuses surface electromyography (sEMG) signals with other sensor data, Xu’s approach significantly reduces the need for extensive manual annotation while maintaining high recognition accuracy. This contribution is particularly impactful for developing more intuitive, efficient, and scalable HRI systems—enabling robots to interpret human gestures in real time with less training overhead. Xu’s work bridges the gap between deep learning practicality and real-world HRI deployment, offering a pathway toward seamless, non-invasive control of robotic systems. With growing interest in wearable sensing and intelligent interfaces, Xu’s research continues to influence the design of adaptive, user-friendly robotic assistants.