Bingfei Fan
Papers
5
Total Citations
46
H-Index
3
About
Bingfei Fan is a rising leader in the field of wearable robotics and human motion analysis, with a focused expertise in developing intelligent algorithms for exoskeleton control and rehabilitation. His research centers on the seamless integration of biomechanical sensing—using inertial sensors, surface electromyography (sEMG), and plantar pressure systems—with advanced deep learning and optimization models. Fan’s major contributions include novel frameworks for sit-to-stand phase identification using a CNN-BiLSTM ensemble with attention mechanisms (22 citations), continuous joint angle prediction via an improved sparrow search algorithm (ISSA) hybrid kernel extreme learning machine (11 citations), and ankle moment estimation from distributed plantar pressure data (9 citations). His work also extends to complete gait phase recognition using muscle synergy and PSO-CNN-LSTM architectures, as well as predicting paretic gait trajectories for stroke rehabilitation. With over 46 cumulative citations in just two years (2024–2025), Fan’s research is already shaping the next generation of intelligent, sensor-driven assistive devices. His achievements highlight a commitment to translating complex sensor data into real-time, actionable control signals for wearable robots, making him a key figure in advancing human–machine interaction and rehabilitation engineering.
Research Focus
Key Achievements
Top Papers
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