TY - GEN
T1 - SEMG-Based Continues Motion Prediction of Shoulder exoskeleton Control Using the VGANet Model
AU - Jiang, Tongxin
AU - Zhang, Fuhai
AU - Yang, Lei
AU - Wu, Tianyang
AU - Fu, Yili
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Wearable exoskeleton robots play a crucial role in promoting upper limb function recovery. To enhance human-robot interaction and achieve precise control, continuous prediction of limb joint angles is required. This paper proposes a decoupled network model (VGANet) based on Variable Graph Convolutional Networks (V-GCN) and Temporal External Attention (TEA) for motion prediction in upper limb rehabilitation training. By establishing a mapping relationship between surface electromyography (sEMG) signals and upper limb movements, the model can predict future joint angles based on real-time sEMG signals. Experimental results demonstrate that this method can achieve continuous motion prediction for the shoulder joint and has been successfully applied to the control system of exoskeleton robots, providing an effective solution for the intelligent development of rehabilitation exoskeletons.
AB - Wearable exoskeleton robots play a crucial role in promoting upper limb function recovery. To enhance human-robot interaction and achieve precise control, continuous prediction of limb joint angles is required. This paper proposes a decoupled network model (VGANet) based on Variable Graph Convolutional Networks (V-GCN) and Temporal External Attention (TEA) for motion prediction in upper limb rehabilitation training. By establishing a mapping relationship between surface electromyography (sEMG) signals and upper limb movements, the model can predict future joint angles based on real-time sEMG signals. Experimental results demonstrate that this method can achieve continuous motion prediction for the shoulder joint and has been successfully applied to the control system of exoskeleton robots, providing an effective solution for the intelligent development of rehabilitation exoskeletons.
UR - https://www.scopus.com/pages/publications/105029972691
U2 - 10.1109/IROS60139.2025.11247119
DO - 10.1109/IROS60139.2025.11247119
M3 - 会议稿件
AN - SCOPUS:105029972691
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 16379
EP - 16384
BT - IROS 2025 - 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems, Conference Proceedings
A2 - Laugier, Christian
A2 - Renzaglia, Alessandro
A2 - Atanasov, Nikolay
A2 - Birchfield, Stan
A2 - Cielniak, Grzegorz
A2 - De Mattos, Leonardo
A2 - Fiorini, Laura
A2 - Giguere, Philippe
A2 - Hashimoto, Kenji
A2 - Ibanez-Guzman, Javier
A2 - Kamegawa, Tetsushi
A2 - Lee, Jinoh
A2 - Loianno, Giuseppe
A2 - Luck, Kevin
A2 - Maruyama, Hisataka
A2 - Martinet, Philippe
A2 - Moradi, Hadi
A2 - Nunes, Urbano
A2 - Pettre, Julien
A2 - Pretto, Alberto
A2 - Ranzani, Tommaso
A2 - Ronnau, Arne
A2 - Rossi, Silvia
A2 - Rouse, Elliott
A2 - Ruggiero, Fabio
A2 - Simonin, Olivier
A2 - Wang, Danwei
A2 - Yang, Ming
A2 - Yoshida, Eiichi
A2 - Zhao, Huijing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2025
Y2 - 19 October 2025 through 25 October 2025
ER -