22 citations · 23 across the 6 of their papers we have counts for
6 papers
Physics-Embedded Neural Networks for sEMG-based Continuous Motion Estimation
Wending Heng, Chaoyuan Liang, Yihui Zhao +3
Accurately decoding human motion intentions from surface electromyography (sEMG) is essential for myoelectric control and has wide applications in rehabilitation robotics and assis…
The bionic neural network for external simulation of human locomotor system
Yue Shi, Shuhao Ma, Yihui Zhao
Muscle forces and joint kinematics estimated with musculoskeletal (MSK) modeling techniques offer useful metrics describing movement quality. Model-based computational MSK models c…
The online learning architecture with edge computing for high-level control for assisting patients
Yue Shi, Yihui Zhao
The prevalence of mobility impairments due to conditions such as spinal cord injuries, strokes, and degenerative diseases is on the rise globally. Lower-limb exoskeletons have been…
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…
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…
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 (…