A NOVEL TECHNIQUE FOR CLASSIFICATION OF MYOELECTRIC SIGNALS FOR PROSTHESIS
Bildiriler > Tam Metin Bildiri · 2002
Yazarlar: B. Karlik, M. O. Tokhi, Musa ALCI
IFAC Proceedings Volumes, , Barcelona, Uluslararası, Davetli Konuşmacı, Tam Metin Bildiri, 2002-2002, cilt 35, ss 447-451
DOI: 10.3182/20020721-6-es-1901.00980
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.