DocumentCode :
2506799
Title :
Myoelectric neural networks signal analysis
Author :
Karlik, Bekir ; Pastaci, Halit ; Korü, Mehmet
fYear :
1994
fDate :
12-14 Apr 1994
Firstpage :
262
Abstract :
The myoelectric signal analysis strategy in such situations is to have the user/subject generate a set of repeatable muscle construction patterns, each having similar characteristic parameters that ran be easily extracted from the myoelectric signal. By using these parameters, it is possible to segregate different muscle contraction patterns into classes. Each class of muscle contraction is used to trigger a particular function in the prosthetic device. A neural network implementation is applied to myoelectric signal analysis tasks. The motivation behind this research is to explore more reliable methods of deriving control for a multidegree of freedom arm prosthesis. Autoregressive model parameters and signal power are used as features
Keywords :
artificial limbs; electromyography; medical signal processing; artificial arm control; autoregressive model parameters; more reliable control methods; multidegree of freedom arm prosthesis; myoelectric neural networks signal analysis; myoelectric signal analysis tasks; prosthetic device function triggering; repeatable muscle construction patterns; signal power; Artificial neural networks; Elbow; Electrodes; Electromyography; Muscles; Neural networks; Neural prosthesis; Prosthetics; Signal analysis; Signal processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrotechnical Conference, 1994. Proceedings., 7th Mediterranean
Conference_Location :
Antalya
Print_ISBN :
0-7803-1772-6
Type :
conf
DOI :
10.1109/MELCON.1994.381098
Filename :
381098
Link To Document :
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