Zhijie Fang

About

Zhijie Fang is a researcher at the forefront of intelligent rehabilitation and neural engineering, specializing in human motion intention recognition and brain-computer interfaces. His work bridges deep learning with biomedical signal processing to advance rehabilitation robotics and early diagnosis of neurodegenerative conditions. Fang’s most cited paper (2021, 7 citations) introduces a CNN-LSTM network that predicts human joint angles using multi-band surface electromyography (sEMG) and historical motion data, directly enabling safer, more responsive active rehabilitation training for paralyzed patients. He further developed a convolutional LSTM model (2019, 5 citations) for motion intention recognition from spatiotemporal EEG data, enhancing real-time human-robot interaction. In a critical application area, Fang applied group feature learning and domain adversarial neural networks (2021, 5 citations) to create an EEG-based diagnostic system for amnestic mild cognitive impairment (aMCI), offering an objective, non-invasive tool for early Alzheimer’s prevention. Collectively, his work demonstrates how deep learning can decode neural and muscular signals to restore movement and detect cognitive decline, with growing impact in assistive robotics and clinical diagnostics.

Research Focus

Key Achievements

Top Papers

1. 1
   CNN-LSTM Network Based Prediction of Human Joint Angles Using Multi-Band SEMG and Historical Angles

   7 citations · 2021
2. 2
   Convolutional LSTM: A Deep Learning Method for Motion Intention Recognition Based on Spatiotemporal EEG Data

   5 citations · 2019
3. 3
   Group Feature Learning and Domain Adversarial Neural Network for aMCI Diagnosis System Based on EEG

   5 citations · 2021

Key Collaborators

Contact & Links