456 citations · 706 across the 32 of their papers we have counts for
Showing eess.SPShow all
3 papers · 1 filter
eess.SP2023★ 1 cited
A Physics-Informed Low-Shot Learning For sEMG-Based Estimation of Muscle Force and Joint Kinematics
Yue Shi, Shuhao Ma, Yihui Zhao +1
Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis of the dynamic interplay among neural muscle st…
eess.SP2022
Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer
Jie Zhang, Yihui Zhao, Tianzhe Bao +5
Data-driven methods have become increasingly more prominent for musculoskeletal modelling due to their conceptually intuitive simple and fast implementation. However, the performan…
eess.SP2022★ 22 cited
Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG
Jie Zhang, Yihui Zhao, Fergus Shone +4
Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (…