Papers
35
Total Citations
507
H-Index
12
About
Longhan Xie is a prominent researcher specializing in rehabilitation robotics, human-machine interfaces, and biosignal processing, with a particular focus on improving outcomes for stroke survivors. His work sits at the intersection of machine learning, neuromuscular sensing, and assistive technology, addressing critical challenges in modern neurorehabilitation. Xie's most impactful contribution — an SVM-based classification system for surface electromyography (sEMG) signals in upper-limb rehabilitation, garnering 112 citations — helped establish a foundation for patient-cooperative robotic control strategies. Building on this, he developed deep learning models for continuous joint angle estimation and attention mechanism-based motion intention prediction, advancing sEMG-driven robot control beyond simple discrete commands toward naturalistic, real-time interaction. A distinctive thread in Xie's research is his focus on compensatory movement detection — identifying when stroke patients unconsciously substitute incorrect movement patterns during therapy. His real-time detection systems using pressure distribution and sEMG signals, cited nearly 65 times collectively, represent a meaningful step toward autonomous, therapist-quality supervision. He has also contributed to EEG-based brain-machine interfaces, individualized gait generation using recurrent neural networks, and federated learning for fault diagnosis. Collectively, his work reflects a comprehensive vision for intelligent, adaptive rehabilitation systems that meaningfully improve patient recovery.
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