Title :
Myoelectric signal recognition using fuzzy clustering and artificial neural networks in real time
Author :
Del Boca, Adrian ; Park, Dong C.
Author_Institution :
Dept. of Electr. & Comput. Eng., Florida Int. Univ., Miami, FL, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
Application of EMG-controlled functional neuromuscular stimulation to a denervated muscle depends largely on the successful discrimination of the myoelectric signal (MES) by which the subject desires to execute control over the impeded movement. This can be achieved by an adaptive and flexible interface that is robust regardless of electrode location, strength of remaining muscle activity or even personal conditions. A real-time application of an artificial neural network that can accurate recognize the MES signature is proposed in this paper. MES features are first extracted through Fourier analysis and clustered using the fuzzy c-means algorithm. Data obtained by this unsupervised learning technique are then automatically targeted and presented to a multilayer perceptron type neural network. For real-time operation, a digital signal processor operates over the resulting set of weights and maps the incoming signal to the stimulus control domain. Results show a highly accurate discrimination of the control signal over interference patterns
Keywords :
Fourier analysis; electromyography; medical signal processing; multilayer perceptrons; muscle; neurophysiology; pattern recognition; unsupervised learning; EMG-controlled functional neuromuscular stimulation; Fourier analysis; MES signature; artificial neural networks; denervated muscle; digital signal processor; fuzzy c-means algorithm; fuzzy clustering; multilayer perceptron; myoelectric signal recognition; stimulus control domain; unsupervised learning; Algorithm design and analysis; Artificial neural networks; Automatic control; Data mining; Electrodes; Feature extraction; Impedance; Muscles; Neuromuscular stimulation; Robustness;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374728