Papers
7
Total Citations
83
H-Index
5
About
Zhe Fu is an emerging researcher at the intersection of robotics, human-robot interaction, and intelligent sensing systems. His work spans three interconnected domains: teleoperation and IoT-enabled robotics, indoor localization using passive RFID technology, and human-machine interfaces for rehabilitation exoskeletons. Fu's most influential contribution, garnering 30 citations, introduced a multisensory fusion system combining haptic and visual feedback for teleoperation under an IoT framework — addressing critical challenges of operator fatigue and accuracy in remote applications such as nursing and semi-mechanical control. Complementing this, his series of RFID-based indoor localization studies — leveraging ISAR-SAR techniques and phase-RSSI fusion methods with mobile robotic platforms — has collectively attracted over 40 citations, demonstrating robust solutions for precise tag positioning in GPS-denied environments. Fu has also made notable strides in rehabilitation robotics, developing an sEMG-based human-exoskeleton interface that fuses convolutional neural networks with hand-crafted features to improve lower-limb movement prediction for hemiplegic patients. More recently, his work on semantic-feature-enhanced LiDAR odometry reflects a broadening expertise in autonomous navigation. With over 80 total citations across seven publications, Fu represents a dynamic voice in next-generation human-robot systems research.
Research Focus
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Top Papers
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