Abstract: Electromyography (EMG) is very important to capture muscle activity. Although many jobs establish data acquisition system, however, it is also essential to demonstrate that these data are reliable. In this sense, one proposes a design and implementation of a data acquisition system with the Myoware device and the ATmega329P microcontroller. One also proved its reliability by classifying the movement of the fingers of the hand, with the help of the algorithm k-Nearest Neighbors (KNN) and the application of Classification Learner code of Matlab. The results show a success rate of 99.1%.

Alvarado-Díaz Witman, Meneses-Claudio Brian and Roman-Gonzalez Avid, “Electromyography Signal Acquisition and Analysis System for Finger Movement Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 10(6), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100653

@article{Witman2019,
title = {Electromyography Signal Acquisition and Analysis System for Finger Movement Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100653},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100653},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {6},
author = {Alvarado-Díaz Witman and Meneses-Claudio Brian and Roman-Gonzalez Avid}
}