Electromyography Signal Acquisition and Analysis System for Finger Movement Classification

Alvarado-Díaz Witman,Meneses-Claudio Brian,Roman-Gonzalez Avid

doi:10.14569/ijacsa.2019.0100653

Alvarado-Díaz Witman, Meneses-Claudio Brian + Show 1 more

Open Access

https://doi.org/10.14569/ijacsa.2019.0100653

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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%.

Highlights

In the national report of the socio-demographic profile1, carried out by the Statistics and Informatics National Institute (INEI for its Spanish name), it mentions that in Peru 10.4% of the population suffers from some disability
Wyoware [1] was used to obtain data, which is an electrodiagnostic system to evaluate and record the electrical activity produced by the skeletal muscles
The Wyoware detects the electrical activity of the muscles, convert them into a variable voltage that can be read on the analog input pin of any microcontroller, in our case the ATmega328P microcontroller

Summary

Introduction

In the national report of the socio-demographic profile, carried out by the Statistics and Informatics National Institute (INEI for its Spanish name), it mentions that in Peru 10.4% of the population suffers from some disability. From this population, 15.1% cannot move or walk. Wyoware [1] was used to obtain data, which is an electrodiagnostic system to evaluate and record the electrical activity produced by the skeletal muscles. These sensors are used in prostheses, robotics, and much more. The Wyoware detects the electrical activity of the muscles, convert them into a variable voltage that can be read on the analog input pin of any microcontroller, in our case the ATmega328P microcontroller

Methods

Results

Conclusion