Binbin Su

KTH Royal Institute of Technology, Zhejiang University of Technology

Papers

Total Citations

203

H-Index

About

Binbin Su is a leading researcher in the intersection of wearable robotics and human motion analysis, with a primary focus on developing intelligent control systems for assistive devices like exoskeletons. Their work centers on two critical challenges: accurately predicting gait trajectories and phases, and enabling real-time recognition of human movement patterns. Su’s most influential contributions include pioneering the use of Long Short-Term Memory (LSTM) networks for lower-body trajectory and gait phase prediction, achieving 88 citations, and applying deep convolutional neural networks to inertial measurement unit data for robust gait phase recognition, cited 73 times. These advances are foundational for phase-based exoskeleton control, allowing devices to deliver precise, context-aware assistance during walking. Additionally, Su’s research on a multisensory-feedback tactile glove, with dense sensing arrays for object recognition (42 citations), demonstrates a broader commitment to human-machine interfaces. With over 200 total citations, Su’s work has significantly advanced the practical deployment of assistance-as-needed robotic technologies, directly impacting rehabilitation and mobility aids. Their innovative fusion of deep learning with biomechanical sensing continues to shape the future of wearable robotics.