Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signal...

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Bibliographic Details
Published in:Future internet 2019-01, Vol.11 (1), p.25
Main Authors: Dorgham, Osama, Al-Mherat, Ibrahim, Al-Shaer, Jawdat, Bani-Ahmad, Sulieman, Laycock, Stephen
Format: Article
Language:English
Subjects:
Amplitudes
artificial neural network
Artificial neural networks
Back propagation
Bioelectricity
Elbow
Electromyography
Feature extraction
Force
forearm human muscles
Fourier transforms
Fuzzy logic
Human mechanics
Internet
Load
Loads (forces)
modeling nerve signals
Muscle fatigue
Muscles
Nervous system
Neural networks
Noise
Predictions
Propagation
Registration
Research parks
Signal processing
smart system
surface electromyography
Time domain analysis
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Summary:Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with various loads. In this paper, we use time domain features to analyze the relationship between the amplitude of SEMG signals and the load. We extract some features (e.g., mean absolute value, root mean square, variance and standard deviation) from the collected SEMG signals to estimate the bicep’ muscle force for the various loads. Further, we use the R-squared value to depict the correlation between the SEMG amplitude and the muscle loads by linear fitting. The best performance the ANN model with 60 hidden neurons for three loads used (3 kg, 5 kg and 7 kg) has given a mean square error of 1.145, 1.3659 and 1.4238, respectively. The R-squared observed are 0.9993, 0.99999 and 0.99999 for predicting (reproduction step) of smooth SEMG signals.
ISSN:1999-5903
1999-5903
DOI:10.3390/fi11010025