Qiyu Li
Papers
2
Total Citations
32
H-Index
2
About
Qiyu Li is a researcher specializing in human-computer interaction (HCI) and biomedical signal processing, with a particular focus on leveraging electromyographic (EMG) signals for intuitive and accessible technology interfaces. Their work sits at the intersection of machine learning and rehabilitation engineering, exploring how surface electromyographic (sEMG) signals — generated by muscle activation — can be harnessed to enable dynamic hand gesture recognition. Li's most notable contributions center on the application of deep learning architectures, specifically CNN-LSTM neural networks, to classify complex, dynamic hand gestures from sEMG data. This hybrid approach combines the spatial feature extraction strengths of convolutional neural networks with the temporal sequence modeling capabilities of long short-term memory networks, representing a meaningful advancement in real-time gesture-based control systems. Their 2022 publication on EMG-based HCI has garnered 21 citations, while a closely related companion study has accumulated 11 citations, reflecting growing community interest in this methodology. The practical implications of Li's research are significant, extending to prosthetics, rehabilitation devices, and assistive technologies for individuals with motor impairments. For students entering the fields of neural interfaces or biomedical engineering, Li's work offers a compelling model of how deep learning can transform biological signals into actionable, human-centered solutions.
Research Focus
Key Achievements
Top Papers
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