Effect of Muscle Fatigue on Heart Signal on Physical Activity with Electromyogram and Electrocardiogram Monitoring Signals

Physical activity is an activity of body movement by utilizing skeletal muscles that are carried out daily. One form of physical activity is exercise which aims to improve health and fitness. Parameters related to health and wellness are heart and muscle activity. Strong and prolonged muscle contractions result in muscle fatigue. The authors used electromyographic (EMG) signals to measure muscle fatigue by monitoring changes in electrical muscle activity. This study aims to analyze the effect of muscle fatigue on cardiac signals during subjects perform physical activity. This research method uses Fast Fourier Transform (FFT) with one group pre-test-post-test research design. The independent variable is the EMG signal when doing plank activities, while the dependent variable is the result of monitoring the EMG signal. The authors use MPF, MDF, and MNF to get more detailed measurement results and perform a T-test. The test results showed a significant value (p-value <0.05) in the pre-test and post-test. The Pearson correlation test got a value of 0.628, indicating a strong relationship between exercise frequency and plank duration. When the respondent experiences muscle fatigue, the heart signal is affected by noise movement artifacts that appear when doing the plank. It is concluded that the device in this study can be used properly. To overcome noise in the EMG signal, it is recommended to use dry electrodes and high-quality components. To improve the ability to transmit data, it is recommended to use a Raspberry microcontroller.

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Journal
Indonesian Journal of Electronics Electromedical Engineering and Medical Informatics
Published
2025-08-17
DOI
https://doi.org/10.35882/ijeeemi.v4i3.149
Citations
1
Primary Topic
Advanced Technologies and Applied Computing
Type
article
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article

Effect of Muscle Fatigue on Heart Signal on Physical Activity with Electromyogram and Electrocardiogram Monitoring Signals

Muhammad Fauzi, Vishwajeet Shankhwar, Sari Luthfiyah, Bahaa Eddine Elbaghazaoui et al.
1 citations
Indonesian Journal of Electronics Electromedical Engineering and Medical Informatics
Advanced Technologies and Applied Computing
article

Effect of Muscle Fatigue on Heart Signal on Physical Activity with Electromyogram and Electrocardiogram Monitoring Signals

Muhammad Fauzi, Vishwajeet Shankhwar, Sari Luthfiyah, Bahaa Eddine Elbaghazaoui, Endro Yulianto, Triwiyanto Triwiyanto, Bambang Guruh Irianto
article en
1 citations

Abstract

Physical activity is an activity of body movement by utilizing skeletal muscles that are carried out daily. One form of physical activity is exercise which aims to improve health and fitness. Parameters related to health and wellness are heart and muscle activity. Strong and prolonged muscle contractions result in muscle fatigue. The authors used electromyographic (EMG) signals to measure muscle fatigue by monitoring changes in electrical muscle activity. This study aims to analyze the effect of muscle fatigue on cardiac signals during subjects perform physical activity. This research method uses Fast Fourier Transform (FFT) with one group pre-test-post-test research design. The independent variable is the EMG signal when doing plank activities, while the dependent variable is the result of monitoring the EMG signal. The authors use MPF, MDF, and MNF to get more detailed measurement results and perform a T-test. The test results showed a significant value (p-value <0.05) in the pre-test and post-test. The Pearson correlation test got a value of 0.628, indicating a strong relationship between exercise frequency and plank duration. When the respondent experiences muscle fatigue, the heart signal is affected by noise movement artifacts that appear when doing the plank. It is concluded that the device in this study can be used properly. To overcome noise in the EMG signal, it is recommended to use dry electrodes and high-quality components. To improve the ability to transmit data, it is recommended to use a Raspberry microcontroller.

Indonesian Journal of Electronics Electromedical Engineering and Medical InformaticsVol. 4(3)
Université Ibn-Tofail (MA), Mohammed Bin Rashid University of Medicine and Health Sciences (AE), Politeknik Kesehatan Kemenkes Semarang (ID), Poltekkes Kemenkes Surabaya
Openalex Percentile: Top 77%
Advanced Technologies and Applied Computing
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