A novel technique for classification of myoelectric signals for prosthesis
Küçük Resim Yok
Tarih
2002
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IFAC Secretariat
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This paper presents an investigation into classifying myoelectric signals using a new fuzzy clustering neural network architecture for control of multifunction prostheses. Moreover, a comparative study of the classification accuracy of myoelectric signals using multi-layer perceptron with back-propagation algorithm, and the new fuzzy clustering neural network (FCNN) is presented. The myoelectric signals considered are used to classify four upper-limb movements, which are elbow flexion, elbow extension, wrist pronation and wrist supination, grasp, and resting. The results suggest that FCNN can generalise better than the multi-layer perceptron without requiring extra computational effort. The proposed neural network algorithm allows the user to learn better and faster. Copyright © 2002 IFAC.
Açıklama
15th World Congress of the International Federation of Automatic Control, 2002 -- 21 July 2002 through 26 July 2002 -- 153189
Anahtar Kelimeler
Fuzzy clustering, Myoelectric signal, Neural network, Pattern recognition
Kaynak
IFAC Proceedings Volumes (IFAC-PapersOnline)
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
35
Sayı
1
