인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Investigating electromyography (EMG) signals is vital to promote the development of both rehabilitative robots and understanding of the movement neural mechanism. Interactions between various muscle units are paramount to be measured through network analysis, aiming to reveal how information is propagated and integrated. Herein, an EMG network using an epidermal array electrode sleeve to record multichannel EMGs is constructed. Then, a master–slave rehabilitation robot by adopting the EMG network as a feature for movement intention recognition is built. The results demonstrate that the sleeve can record signals with high quality, characterized by better signal robustness and higher movement recognition performance. The different finger movements evoke the specific spatial network patterns, characterized by the dominated hub at the muscle in charge of the corresponding movement, and the proposed EMG network‐based approach consistently achieves the highest recognition accuracy. Moreover, the proposed approach also shows the relatively less influence of signal length and electrode positions on the movement recognition. Finally, the proposed robot system can achieve 98.21% ± 2.37 accuracy for online control. These results provide a novel theoretical and practical basis for neural prosthesis control and hemiplegic hand rehabilitation.
#Electromyography
#Robustness (evolution)
#Robot
#Wearable computer
#Artificial neural network
#Computer science
#Artificial intelligence
#SIGNAL (programming language)
#Functional movement
#Movement (music)
#Computer vision
#Engineering
#Speech recognition
#Physical medicine and rehabilitation
#Pattern recognition (psychology)
#Medicine
#Acoustics
#Embedded system
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