TY - GEN
T1 - Using Upper Limb Carrying Exoskeleton with Dual-Model Torque Control Strategy to Reduce Load Impact
AU - Liu, Daming
AU - Li, Ye
AU - Liu, Junchen
AU - Wang, Ziqi
AU - Zhao, Jie
AU - Zhu, Yanhe
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Exoskeleton technology holds significant promise within the human-centric paradigm of Industry 5.0 for mitigating work-related musculoskeletal disorders (WMSDs). However, existing systems often struggle with mismatched assistive torque and inefficient human-machine collaboration under dynamic loading conditions, largely due to insufficient motion intent recognition accuracy. This study proposes a dual-model-based multimodal fusion control strategy that integrates a bidirectional LSTM neural network (Bi-LSTM) with a transformer-based multi-task learning model (MTL) to enable real-time torque compensation and accurate prediction of dynamic load mass under varying conditions. The team developed a lightweight elbow joint exoskeleton prototype, leveraging multi-modal information to enhance assistive torque prediction accuracy. Experimental results show an 83.7% reduction in agonist muscle activation under a 3.5 kg load compared to conditions without the exoskeleton, underscoring its potential for industrial material handling scenarios.
AB - Exoskeleton technology holds significant promise within the human-centric paradigm of Industry 5.0 for mitigating work-related musculoskeletal disorders (WMSDs). However, existing systems often struggle with mismatched assistive torque and inefficient human-machine collaboration under dynamic loading conditions, largely due to insufficient motion intent recognition accuracy. This study proposes a dual-model-based multimodal fusion control strategy that integrates a bidirectional LSTM neural network (Bi-LSTM) with a transformer-based multi-task learning model (MTL) to enable real-time torque compensation and accurate prediction of dynamic load mass under varying conditions. The team developed a lightweight elbow joint exoskeleton prototype, leveraging multi-modal information to enhance assistive torque prediction accuracy. Experimental results show an 83.7% reduction in agonist muscle activation under a 3.5 kg load compared to conditions without the exoskeleton, underscoring its potential for industrial material handling scenarios.
UR - https://www.scopus.com/pages/publications/105029944585
U2 - 10.1109/IROS60139.2025.11247416
DO - 10.1109/IROS60139.2025.11247416
M3 - 会议稿件
AN - SCOPUS:105029944585
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 8224
EP - 8231
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 -