Yan Liu

Yan Liu

PhD

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences

Yan Liu - Academic Homepage

Affiliated Institution:

I am a Ph.D. student enrolled in the Pattern Recognition and Intelligent Systems program at the University of Chinese Academy of Sciences (UCAS), and my research work is carried out at the Neuroengineering Research Center, Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences （CAS）.

Research Field:

My research integrates pattern recognition, machine learning, and neuroengineering. It primarily focuses on decoding movement intention from biosignals like surface electromyography (sEMG), developing robust cross-individual adaptive algorithms, and investigating control strategies for intelligent prosthetics. The ultimate goal of my work is to enhance the naturalism and practicality of human-machine interaction through AI technologies, thereby contributing to the advancement of intelligent rehabilitation and assistive systems.

Research Interests:

  1. Multimodal Biosignal Processing and Analysis: Specializing in surface electromyography (sEMG) and force myography (FMG), with active exploration of multimodal data fusion (e.g., ultrasound, muscle-vascular activity) to enhance motion intent decoding.

  2. Intelligent Pattern Recognition and Adaptive Algorithms: Addressing key challenges in EMG pattern recognition, such as cross-individual adaptation and force variation robustness. Proficient in developing novel deep learning (e.g., Transformer, attention-based CNN) and machine learning algorithms for high-accuracy hand gesture and continuous force estimation.

  3. Advanced Sensing Technology and Wearable Devices: Contributing to the development of innovative flexible sensors, including stretchable multi-channel ionotronic electrodes and integrated multimodal wearable patches for in situ monitoring of neuromuscular and vascular activities.

  4. Intelligent Prosthetic System Integration and Application: Research outcomes are directly applied to prosthetic control systems for amputees, enabling simultaneous gesture recognition and continuous grasp force estimation toward clinical translation.

Academic Achievements:

I have published multiple research articles in high-impact international journals such as Journal of Neural Engineering, IEEE Transactions on Neural Systems and Rehabilitation Engineering, IEEE Transactions on Instrumentation and Measurement et.al.