EMG Signal Analysis on Flexion Extension Movements of The Hand and Leg Using Matlab

  • Destra Andika Pratama Politeknik Negeri Sriwijaya, Sumatera Selatan, Indonesia
  • Yeni Irdayanti Politeknik Negeri Sriwijaya, Sumatera Selatan, Indonesia
  • Satrio Aditiyas Sukardi Politeknik Negeri Sriwijaya, Sumatera Selatan, Indonesia
Keywords: Matlab, Muscle, EMG Signal, Extension, Flexion

Abstract

Muscle Spiker Shield is a tool used to record electrical signals generated by the muscles of the human body. These signals can provide important information about the health and activities of organisms, especially humans. As technology advances, more and more devices can be used to record the activity of these signals, including the Muscle Spiker Shield. One of the uses of the Muscle Spiker Shield is to monitor muscle wave activity. Human muscle waves are electrical signals generated by muscles and can provide information about the state of a person's movement activity. Monitoring human muscle wave activity can help in various fields, such as medicine, psychology, and sports. Currently, an electromyograph has been developed which functions as a voltage meter for all muscles to detect muscles in a state of tension and relaxation with the help of a microcontroller. On the Electromyography signal output then to the Arduino uno microcontroller. When using the Muscle Spiker Shield tool with MATLAB, the signals recorded by the tool are imported into the MATLAB software. Then, the data can be processed using various signal analysis techniques, such as filtering, peak detection and statistical processing. Some of the applications that can be done are monitoring leg and hand muscle wave activity during meditation, monitoring muscle wave activity to determine a person's movements, and monitoring muscle wave activity during exercise.

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References

A. Atina, “Aplikasi Matlab pada Teknologi Pencitraan Medis,” J. Penelit. Fis. dan Ter., vol. 1, no. 1, p. 28, 2019, doi: 10.31851/jupiter.v1i1.3123.

R. Maulana and R. R. M. Putri, “Pengkondisian Sinyal Electromyography sebagai Identifikasi Jenis Gerakan Lengan Manusia,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 3, p. 297, 2018, doi: 10.25126/jtiik.201853829.

I. Mohamad, C. Wahyu, and A. Mochammad, “Studi Klasifikasi Tujuh Gerakan Tangan Sinyal Electromyography (EMG) Menggunakan Metode Pattern Recognition,” J. Tek. Mesin S-1, vol. 4, no. 3, pp. 307–316, 2016, [Online]. Available: http://ejournal-s1.undip.ac.id/index.php/jtm

M. A. A. Kadir et al., “Sistem Kontrol Tangan Robot Mengunakan Sinyal Emg Berbasis Mikrokontroller Arduino,” Sci. Eng. Natl. Semin. 4 (SENS 4), vol. 4, no. Sens 4, pp. 417–421, 2019.

N. A. Fahmi, A. Widodo, N. Kholis, and F. Baskoro, “Rancang Bangun Elektromiograf untuk Identifikasi Gerakan Otot Bisep,” J. Tek. Elektro, vol. 10, no. 3, pp. 609–618, 2021.

D. S. Pamungkas, “Penggunaan Kernel SVM untuk Klasifikasi Pergerakan Jari Mengunakan Sinyal EMG,” J. Elektro dan Mesin Terap., vol. 7, no. Vol. 7 No. 2 (2021), pp. 1–6, 2021, doi: 10.35143/elementer.v7i2.5146.

I. Rahayuningsih, A. D. Wibawa, and E. Pramunanto, “Klasifikasi Bahasa Isyarat Indonesia Berbasis Sinyal EMG Menggunakan Fitur Time Domain (MAV, RMS, VAR, SSI),” J. Tek. ITS, vol. 7, no. 1, 2018, doi: 10.12962/j23373539.v7i1.29967.

W. Muldayani, A. M. N. Imron, K. Anam, S. Sumardi, W. Widjonarko, And Z. E. Fitri, “Pengenalan Pola Sinyal Electromyography (EMG) pada Gerakan Jari Tangan Kanan,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 8, no. 3, p. 591, 2020, doi: 10.26760/elkomika.v8i3.591.

D. Pamungkas, S. R. Kurniawan, and B. F. Simamora, “Perbandingan Antara Domain Waktu dan Frekuensi untuk Pengenalan Sinyal EMG,” J. Rekayasa Elektr., vol. 17, no. 1, pp. 36–41, 2021, doi: 10.17529/jre.v17i1.16844.

D. S. Putra, A. D. Wibawa, and M. H. Purnomo, “Klasifikasi Sinyal Emg Pada Otot Tungkai Selama Berjalan Menggunakan Random Forest,” J. Inotera, vol. 1, no. 1, p. 51, 2017, doi: 10.31572/inotera.vol1.iss1.2016.id7.

S. Wangko, “JARINGAN OTOT RANGKA Sistem membran dan struktur halus unit kontraktil,” J. Biomedik, vol. 6, no. 3, 2014, doi: 10.35790/jbm.6.3.2014.6330.

F. M. S. Nursuwars, F. Fathurrohman, F. Awaludin, and A. Sarah, “Narrative Review: Electromyography sebagai Pengendali Lengan Prostetik,” E-JOINT (Electronica Electr. J. Innov. Technol., vol. 1, no. 2, pp. 31–35, 2020, doi: 10.35970/e-joint.v1i2.389.

Patil, Shailaja, and Shubhangi Patil. "Surface electromyography (sEMG) based pain intensity measurement using SVM algorithm." AIP Conference Proceedings. Vol. 2717. No. 1. AIP Publishing, 2023.

Larivière, Christian, Denis Gagnon, and Patrick Loisel. "The comparison of trunk muscles EMG activation between subjects with and without chronic low back pain during flexion–extension and lateral bending tasks." Journal of electromyography and kinesiology 10.2 (2000): 79-91.

Published
2023-09-29
How to Cite
Pratama, D. A., Irdayanti, Y., & Sukardi, S. A. (2023). EMG Signal Analysis on Flexion Extension Movements of The Hand and Leg Using Matlab. Radiasi : Jurnal Berkala Pendidikan Fisika, 16(2), 61-70. https://doi.org/10.37729/radiasi.v16i2.3373
Section
Articles