A hybrid non-invasive method for the classification of Amputee’s hand and wrist movements

Samuel，Oluwarotimi Williams1,2; Li，Xiangxin1,2; Zhang，Xu3; Wang，Hui2; Li，Guanglin1

10.1007/978-981-10-4505-9_34

2019

1680-0737

IFMBE Proceedings

64

161-166

Haikou, China

Springer Verlag

The quest to develop dexterous artificial arm which supports multiple degrees of freedom for amputees has attracted a lot of study interest in the last few decades. The outcome of some of the studies had identified surface Electromyography (EMG) as the most commonly used biological signal in predicting the motion intention of an amputee. Different EMG based control methods for multifunctional prosthesis have been proposed and investigated in a number of previous studies. However, no any multifunctional prostheses are clinically available yet. One of the possible reasons would be that the residual muscles after amputations might not produce sufficient EMG signals for movement classifications. In this study, we proposed to use electroencephalography (EEG) signals recorded from the scalp of an amputee as additional signals for motion identifications. The performance of a hybrid scheme based on the combination of EMG and EEG signals in identifying different hand and wrist movements was evaluated in one transhumeral amputee. Our pilot results showed that the proposed hybrid method increased the classification accuracy in identifying different hand and wrist movements of the amputee. This suggests that the proposed method may have potential to improve the control of multifunctional prostheses.

其他

英语

EI

Chinese Academy of Sciences[] ; [#JCYJ20130402113127532] ; National Basic Research Program of China (973 Program)[2013CB329505] ; National Natural Science Foundation of China[61135004]

20190206368403

Artificial limbs ; Degrees of freedom (mechanics) ; Electroencephalography ; Electrophysiology ; Noninvasive medical procedures ; Palmprint recognition

Biomedical Engineering:461.1 ; Medicine and Pharmacology:461.6 ; Prosthetics:462.4 ; Information Theory and Signal Processing:716.1 ; Computer Applications:723.5 ; Mechanics:931.1

2-s2.0-85059735993

Scopus

1.Key Laboratory of Human-Machine Intelligence-Synergy SystemsChinese Academy of SciencesShenzhen Institutes of Advanced Technology,Shenzhen,518055,China
2.Shenzhen College of Advanced TechnologyUniversity of Chinese Academy of Sciences,Shenzhen,518055,China
3.Department of BiologySouth University of Science and Technology,Shenzhen,China

Samuel，Oluwarotimi Williams,Li，Xiangxin,Zhang，Xu,等. A hybrid non-invasive method for the classification of Amputee’s hand and wrist movements[C]:Springer Verlag,2019:161-166.