Gym training muscle fatigue monitoring using EMG myoware and ar…

International Journal of Reconfigurable and Embedded Systems (IJRES)

Vol. 12, No. 3, November 2023, pp. 345~350

ISSN: 2089-4864, DOI: 10.11591/ijres.v12.i3.pp345-350  345

Journal homepage: http://ijres.iaescore.com

Gym training muscle fatigue monitoring using EMG myoware

and arduino with envelope and sliding window methods

Sena Sukmananda Suprapto1, Vicky Andria Kusuma1,Mifta Nur Farid1,

Muhammad Agung Nursyeha1, Kharis Sugiarto1, Aji Akbar Firdaus2,

Dimas Fajar Uman Putra3

1Department of Electrical Engineering, Institut Teknologi Kalimantan, Balikpapan, Indonesia

2Department of Engineering, Universitas Airlangga, Surabaya, Indonesia

3Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

Article Info

ABSTRACT

Article history:

Received Dec 16, 2022

Revised Mar 21, 2023

Accepted Apr 4, 2023

Muscles are an important organ in the movement of the body's skeleton to

carry out sports activities. Measurement of muscle activity during the

exercise process can be done using electromyography (EMG). This research

uses Myoware muscle sensor (AT-04-001) which is integrated with Arduino

Uno and Xbee to monitor biceps brachii muscle fatigue wirelessly. Fatigue

data processing is carried out objectively using the envelope and sliding

window method and subjectively verbally from the respondents. From this

study, it was found that muscle fatigue can be measured using the method

objectively when there is an increase in EMG amplitude with a window size

of 5 s. The indication of biceps brachii muscle fatigue for the right arm is

stronger to withstand the load during exercise with the average duration of

the measurement of the right arm is 41.87 s from 69.67 s; 53.53 s from

98.90 s and 76.87 s from 98.80 s with the ratio of the left arm tending to

fatigue more quickly is 23.53 s from 42.13 s; 41.87 s from 51.60 s and

23.53 s from 44.73 s.

Keywords:

Arduino uno

Electromyography

Envelope

Gym

Sliding window method

This is an open access article under the CC BY-SA license.

Corresponding Author:

Aji Akbar Firdaus

Department of Engineering, Universitas Airlangga

Surabaya, Indonesia

Email: aji.akbar.firdaus11@gmail.com

1. INTRODUCTION

Muscle fatigue is a condition where muscles experience a decrease in strength that occurs in

response to contractile activity. Muscle fatigue can reduce the efficiency of muscles and can cause

temporary, permanent damage to paralysis [1]–[3]. Symptoms of muscle fatigue can be identified using a tool

called electromyography (EMG), the process of recording the dielectric activity of muscles to determine

whether or not they are contracting. Several studies have shown that EMG signal amplitude gradually

increases with fatigue in the time domain [4]–[7]. The Myoware muscle sensor (AT-04-001) is the latest

electromyography or EMG sensor from advancer technologies. The company has developed a sensor that can

be used directly by attaching electrodes directly to the Myoware module, which can be connected to display

amplified, rectified, and integrated EMG signal data [8]–[11].

This study will aim to monitor the condition of muscles in sports activities and design a prototype of

muscle condition during sports using Myoware (AT-04-001) sensor, using Arduino Uno microcontroller and

Zigbee as radio frequency data communication component. The reading data from the Myoware (AT-04-001)

sensor is stored through an application that has been created that allows users to retrieve envelope signals that

can help individuals see the ability of the bicep brachii muscle to perform physical activities, monitoring

... Previous research has focused on the development of fall detection systems, which can alert family members or caregivers when a fall has occurred [5]. However, these systems do not address the issue of protecting the individual after a fall has occurred [6]. ...

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