Nan Zheng

About

Nan Zheng is a leading researcher in biomedical signal processing and human–computer interaction, with a focus on decoding muscle activity for wearable assistive technologies. Her work centers on surface electromyography (sEMG), where she has pioneered transfer learning approaches to extract low-frequency sEMG signals for robust, low-cost applications. Her most-cited paper, “Transfer Learning-Based Muscle Activity Decoding Scheme by Low-frequency sEMG for Wearable Low-cost Application” (2021), has garnered 24 citations and demonstrates how machine learning can enhance the reliability of neural prostheses and exoskeleton control. Zheng’s contributions are critical for making HCI systems more accessible and practical in real-world settings, particularly for individuals with motor impairments. By reducing computational demands and improving signal interpretation, her research bridges the gap between laboratory innovation and wearable, low-power devices. Her work not only advances the field of neural engineering but also holds promise for revolutionizing rehabilitation and assistive robotics, offering a pathway to more intuitive and affordable human–machine interfaces.

Research Focus

Key Achievements

Top Papers

1. 1
   Transfer Learning-Based Muscle Activity Decoding Scheme by Low-frequency sEMG for Wearable Low-cost Application

   24 citations · 2021

Key Collaborators

Contact & Links