Interpretarea semnalelor emg utilizând reţele neuronale artificiale
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2024-03-15 14:26
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CAPRITA, Horia, NEAMŢU, Mihai-Bogdan, MATACUTA, Ioana, BODRUG, Nicolae, DOBROTĂ, Luminiţa, NEAMŢU, Mihai-Leonida. Interpretarea semnalelor emg utilizând reţele neuronale artificiale . In: Revista ştiinţifico-practică ”Info-Med” , 2013, nr. 1(21), pp. 36-40. ISSN 1810-3936.
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Revista ştiinţifico-practică ”Info-Med”
Numărul 1(21) / 2013 / ISSN 1810-3936

Interpretarea semnalelor emg utilizând reţele neuronale artificiale

Pag. 36-40

Caprita Horia123, Neamţu Mihai-Bogdan213, Matacuta Ioana312, Bodrug Nicolae2, Dobrotă Luminiţa132, Neamţu Mihai-Leonida213
 
1 Universitatea „Lucian Blaga“, Sibiu,
2 Universitatea de Stat de Medicină şi Farmacie „Nicolae Testemiţanu“,
3 Spitalul Clinic de Pediatrie, Sibiu
 
Disponibil în IBN: 30 noiembrie 2013


Rezumat

Human computer interaction is a rapid developing field. Human biosignals can be interpreted by computers (processors) working after mathematical algorithms developed by neurophysiologists, mathematicians and computer scientists. The perceptron(a single layer artificial network) has a supervised learning algorithm capability. Multilayer neural network is a system of perceptrons with an Error Back Propagation learning algorithm which adjusts the sinaptic weights. A special type of neural networks is represented by SOFM-Self organizing feature Map consisting of 2 layers of interconnections). For each neuron there is a cluster of vectors for inputs and one output. Human biosignal (EMG,EEG,EOG) have variables patterns and represents input vectors for these which learn to recognize a biosignal and transmit this in a specialised programme of a computer. The article is a review regarding the accuracy of different types of neural networks (Back propagation neural network, Log-Linearised Gaussian Mixture Network, Fuzzy Mean Max Neural Network, Radial Basis Function Artificial Neural Network, Hidden Markov Model, Bayes networks) to classify the EMG signal and the capability to transmit the results to the main computer.

Cuvinte-cheie
human biosignals, EOG,

EMG, neural networks., EEG