Abstract
Purpose of this work is to classify three different muscle types. For this purpose, the electromyogram (EMG) signals were recorded from biceps, frontallis, abductor pollisis brevis muscles. For the modelling of EMG signals, Autoregressive models used and Autoregressive coefficients used to train and test several Artificial Neural Networks (ANNs). The results of experiments show that Radial Basis Function neural network has 93,3% accuracy to classificate the muscles. After this classifying stage the next step will be the diagnosis of Neuropathy dissease which is defined as the communication damage of nerves between organs and tissue.
Keywords
Autoregressive model
Biceps
Artificial neural network
Electromyography
Computer science
Pattern recognition (psychology)
Biceps brachii muscle
Artificial intelligence
Physical medicine and rehabilitation
Speech recognition
Medicine
Mathematics
Statistics
Subject Areas
Muscle activation and electromyography studies
·Biomedical Engineering ·Physical Sciences
Neuroscience and Neural Engineering
·Cellular and Molecular Neuroscience ·Life Sciences
Advanced Sensor and Energy Harvesting Materials
·Biomedical Engineering ·Physical Sciences
Citations by Year
2020: 1
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