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Article
EMG Based Classification of Percentage of Maximum Voluntary Contraction Using Artificial Neural Networks
Proceedings for IEEE Dallas Circuits and Systems Conference
  • Stephen D Hickman, Georgia Southern University
  • Rocio Alba-Flores, Georgia Southern University
  • Mohammad Ahad, Georgia Southern University
Document Type
Conference Proceeding
Publication Date
10-12-2014
Abstract
This paper presents an application of an Artificial Neural Network (ANN) for the classification of Electromyography (EMG) signals. The classification system has been designed to classify the percentage of maximum voluntary contraction (%MVC) from the bicep muscle. The EMG signals used in this study have been generated using a computer muscle model. Three statistical input features are extracted from the EMG signals and different structures of ANNs and training algorithms have been considered in the study. A 16 neuron hidden layer architecture trained with the scaled conjugate gradient algorithm has been found to be more efficient than the other ANN architectures tested in classifying 9 different bicep muscle contraction levels as a unit of %MVC than other ANN architectures. The ultimate goal of this research is to design a robotic system for people with disabilities and the elderly by utilizing muscle contraction levels as the input of tasks for the robot
Citation Information
Stephen D Hickman, Rocio Alba-Flores and Mohammad Ahad. "EMG Based Classification of Percentage of Maximum Voluntary Contraction Using Artificial Neural Networks" DallasProceedings for IEEE Dallas Circuits and Systems Conference (2014)
Available at: http://works.bepress.com/mohammad-ahad/16/