A Hierarchical Fusion Strategy Based on EEG and sEMG for Human-Exoskeleton System

被引:0
|
作者
Li, Mengyao [1 ,2 ,3 ]
Duan, Shengcai [1 ,2 ,3 ]
Dong, Yao [1 ,2 ,3 ]
Wang, Can [1 ,2 ]
Feng, Wei [1 ,2 ]
Wu, Xinyu [1 ,2 ,4 ]
机构
[1] Chinese Acad Sci, Shenchen Inst Adv Technol, Guangdong Prov Key Lab Robot & Intelligent Syst, Shenzhen 518055, Peoples R China
[2] Chinese Acad Sci, Shenzhen Inst Adv Technol, CAS Key Lab Human Machine Intelligence Synergy Sy, Shenzhen 518055, Peoples R China
[3] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[4] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Hong Kong, Peoples R China
关键词
REHABILITATION;
D O I
10.1109/rcar49640.2020.9303041
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Most of the human spinal cord injuries result in lower-limb disability. To integrate the brain's holistic motion intention with sEMG's control reliability for lower-limb motion neurorehabilitation with the exoskeleton, an ingenious hierarchical fusion strategy based on EEG and sEMG for ambulation with obstacle crossing task was proposed. Specifically, EEG delivers senior task-specific commands to execute refined locomotion while the specific implementation is determined by sEMG. The practical feasibility of ridge regression-based EEG decoding was proved firstly via offline and online methods involving 3 subjects, with subsequent online verification of the proposed strategy yielding higher than 85% of average accuracy. Furthermore, one subject was invited to wear the exoskeleton for the practical application of the strategy.
引用
收藏
页码:458 / 463
页数:6
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