Xingjian Chen

Huazhong University of Science and Technology

Papers

Total Citations

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About

Xingjian Chen is a pioneering researcher in human-robot interaction and wearable robotics, with a focus on developing intelligent sensing systems that bridge the gap between humans and machines. His primary research areas include lower-limb motion intent recognition, soft sensor design, and human-machine collaboration. Chen’s major contributions lie in advancing sensor fusion and machine learning techniques for wearable robot control. His most-cited work, “Lower Limb Motion Intent Recognition Based on Sensor Fusion and Fuzzy Multitask Learning” (2024, 28 citations), addresses the critical challenge of noisy electromyogram (EMG) signals by integrating multiple sensors and fuzzy logic, significantly improving motion prediction accuracy for prosthetic and exoskeleton control. Additionally, his innovative “Air-Chamber-Based Soft Six-Axis Force/Torque Sensor for Human–Robot Interaction” (2023, 7 citations) introduces a novel soft, compliant sensor that mitigates cross-axis coupling issues, enabling safer and more precise force measurement in physical human-robot interaction. Chen’s work is notable for its practical impact on assistive technologies and collaborative robotics, earning recognition for its potential to enhance the safety and intuitiveness of human-robot systems. His research continues to inspire advances in intelligent sensing and adaptive control for next-generation wearable robots.