A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis
Küçük Resim Yok
Tarih
2003
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee-Inst Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Accurate and computationally efficient means of classifying surface myoelectric signals has been the subject of considerable research effort, in recent years. The aim of this paper is to classify myoelectric signals using new fuzzy clustering neural network (NN) architectures to control multifunction prostheses. This paper presents a comparative study of the classification accuracy of myoelectric signals using multilayered perceptron NN using back-propagation, conic section function NN, and new fuzzy clustering NNs (FCNNs). The myoelectric signals considered are used in classifying six upper-limb movements: elbow flexion, elbow extension, Wrist pronation and wrist supination, grasp, and resting. The results suggest that FCNN can generalize better than other NN algorithms and help the user learn, better and faster. This method has the potential of being very efficient in real-time applications.
Açıklama
Anahtar Kelimeler
fuzzy clustering, myoelectric signal, neural network, pattern recognition, upper-limb prosthesis
Kaynak
Ieee Transactions on Biomedical Engineering
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
50
Sayı
11
