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

1
Department of Computer Information Systems, Prince Abdullah Ben Ghazi Faculty of Information Technology, AL-Balqa Applied University, Al-Salt 19117, Jordan
2
School of Computing Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
*
Author to whom correspondence should be addressed.
Future Internet 2019, 11(1), 25; https://doi.org/10.3390/fi11010025
Received: 2 December 2018 / Revised: 25 December 2018 / Accepted: 2 January 2019 / Published: 21 January 2019
(This article belongs to the Special Issue Security and Privacy in Information and Communication Systems)
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Abstract

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. View Full-Text
Keywords: smart system; surface electromyography; modeling nerve signals; forearm human muscles; artificial neural network smart system; surface electromyography; modeling nerve signals; forearm human muscles; artificial neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Dorgham, O.; Al-Mherat, I.; Al-Shaer, J.; Bani-Ahmad, S.; Laycock, S. Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network. Future Internet 2019, 11, 25.

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