Wenxi Chen
Papers
3
Total Citations
8
H-Index
2
About
Wenxi Chen is a researcher at the intersection of rehabilitation robotics and machine learning, with a focus on developing adaptive, data-efficient systems for stroke recovery. Their major contributions center on intent inferral for robotic hand orthoses, addressing the critical challenge of limited labeled training data from disabled-bodied subjects. Chen proposed MetaEMG, a meta-learning framework that enables fast adaptation in intent inferral, allowing classifiers to generalize across varying muscle tone conditions with minimal new data. Additionally, they introduced ChatEMG, a synthetic data generation approach that overcomes the high variability of EMG signals across sessions and subjects, reducing the need for extensive real-world data collection. These works, published in 2024, have already garnered several citations, reflecting their timely impact on assistive robotics. Chen’s innovative use of synthetic data and meta-learning not only advances rehabilitation technology but also sets a precedent for scalable, personalized human-machine interfaces, making stroke therapy more accessible and effective.
Research Focus
Key Achievements
Top Papers
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