Design and Implementation of an Arduino Nano-Based Pinky Finger EMG and Eye EOG Physiological Signal Generator
Rancang Bangun Generator Sinyal Fisiologis EMG Jari Kelingking dan EOG Mata Berbasis Arduino Nano
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Hendi Handian Rachmat(1*)
Program Studi Teknik Elektro Institut Teknologi Nasional Bandung
(*) Corresponding Author
Keywords:
Arduino Nano, EMG, EOG, physiological signal,signal generator
Abstract
In this study, the design and implementation of a portable Arduino Nano-based physiological signal generator for pinky EMG and EOG eye movement (looking right and left) were carried out. The purpose of this study is to develop a device capable of storing and generating physiological signals, so that the device can serve as a substitute for test subjects. The physiological signal generator system was implemented by storing digital physiological signal data from test subjects in the internal memory of the Arduino Nano microcontroller, which also acts as the system's controller and processor. The signal conditioning system uses an MCP4725 module 12-bit resolution DAC, an LM358 OP-Amp as a non-inverting signal amplifier, a 20x4 LCD as the signal menu display, and a joystick module for selecting signal menus. The system was tested by comparing the signals generated by the signal generator with the physiological signal data recorded from test subjects (the data stored on the device). Based on the test results, the signal generated by the electronic device is visually ±100% identical to the recorded physiological signal data. The average amplitude difference is relatively small, with the smallest difference being 0.1 mV and the largest difference being 4.9 mV.
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References
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[2] A. Kaur, “Wheelchair control for disabled patients using EMG/EOG based human machine interface: a review,” J. Med. Eng. Technol., vol. 45, no. 1, pp. 61–74, 2021, doi: 10.1080/03091902.2020.1853838. [Online]. Available:https://pubmed.ncbi.nlm.nih.gov/33302770/
[3] N. Barbara, T. A. Camilleri, and K. P. Camilleri, “A comparison of EOG baseline drift mitigation techniques,” Biomed. Signal Process. Control, vol. 57, p. 101738, 2020, doi: 10.1016/j.bspc.2019.101738. [Online]. Available:https://www.sciencedirect.com/science/article/abs/pii/S1746809419303192
[4] A. Gawad et al., “EOG acquisition system based on ATmega AVR microcontroller,” J. Ambient Intell. Humaniz. Comput., vol. 14, no. 12, pp. 16589–16605, 2023, doi: 10.1007/s12652-023-04622-9. [Online]. Available:https://link.springer.com/content/pdf/10.1007/s12652-023-04622-9.pdf
[5] T. Triadi, I. Wijayanto, and S. Hadiyoso, “Electrooculogram (EOG) based Mouse Cursor Controller Using the Continuous Wavelet Transform and Statistic Features,” Lontar Komput. J. Ilm. Teknol. Inf., vol. 12, no. 1, p. 53, 2021, doi: 10.24843/lkjiti.2021.v12.i01.p06. [Online]. Available:https://ojs.unud.ac.id/index.php/lontar/article/view/70269/39034
[6] H. Vieira, N. Costa, J. F. A. Alves, and L. P. Coelho, “Simulation of Abnormal Physiological Signals in a Phantom for Bioengineering Education,” Int. J. Online Biomed. Eng., vol. 16, no. 14, pp. 107–121, 2020, doi: 10.3991/ijoe.v16i14.16941. [Online]. Available: https://online-journals.org/index.php/i-joe/article/view/16941/8289
[7] Z. Ahmad and N. Khan, “A Survey on Physiological Signal-Based Emotion Recognition,” Bioengineering, vol. 9, no. 11, 2022, doi: 10.3390/bioengineering9110688. [Online]. Available: https://www.mdpi.com/2306-5354/9/11/688
[8] M. R. Kose, M. K. Ahirwal, and A. Kumar, “A new approach for emotions recognition through EOG and EMG signals,” Signal, Image Video Process., vol. 15, no. 8, pp. 1863–1871, 2021, doi: 10.1007/s11760-021-01942-1. [Online]. Available: https://link.springer.com/article/10.1007/s11760-021-01942-1
[9] A. Pudji, R. Mak’ruf, and W. Wirasa, “Design and Build ECG Simulator,” Int. J. Sci. Res., vol. 8, no. 10, pp. 1084–1087, 2018, [Online]. Available: www.ijsr.net [Online]. Available: https://jurusankebidanan.poltekkesdepkes-sby.ac.id/wp-content/uploads/2020/01/3.Design-and-Build.pdf
[10] W. Muldayani, A. M. N. Imron, K. Anam, S. Sumardi, W. Widjonarko, and Z. V. 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. [Online]. Available: https://ejurnal.itenas.ac.id/index.php/elkomika/article/view/3646/2376
[11] K. Sharma, N. Jain, and P. K. Pal, “Detection of eye closing/opening from EOG and its application in robotic arm control,” Biocybern. Biomed. Eng., vol. 40, no. 1, pp. 173–186, 2020, doi: 10.1016/j.bbe.2019.10.004. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0208521619304723
[12] Cahyo, O. B. D. C., & Kholis, N. (2019). Rancang Bangun Simulator Elektrokardiogram Menggunakan FPGA yang Terintegrasi dengan Software Python. Jurnal Teknik Elektro, 08(03), 619–625. [Online]. Available: https://ejournal.unesa.ac.id/index.php/JTE/article/view/29276/26812
[13] J. C. Edelmann, D. Mair, D. Ziesel, M. Burtscher, and T. Ussmueller, “An ECG simulator with a novel ECG profile for physiological signals,” J. Med. Eng. Technol., vol. 42, no. 7, pp. 501–509, 2018, doi: 10.1080/03091902.2019.1576788. [Online]. Available:https://pubmed.ncbi.nlm.nih.gov/30773952/
[14] M. J. Burke and M. Nasor, “An accurate programmable ECG simulator,” J. Med. Eng. Technol., vol. 25, no. 3, pp. 97–102, 2001, doi: 10.1080/03091900110051640. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/11530829/
[15] S. E. De Lucena, “ECG simulator for testing and servicing cardiac monitors and electrocardiographs,” 18th IMEKO TC4 Symp. Meas. Electr. Quant. 2011, Part Metrol. 2011, pp. 109–112, 2011. [Online]. Available: https://www.imeko.org/publications/tc4-2011/IMEKO-TC4-2011-024.pdf
[16] A. J. Golparvar and M. K. Yapici, “Graphene Smart Textile-Based Wearable Eye Movement Sensor for Electro-Ocular Control and Interaction with Objects,” J. Electrochem. Soc., vol. 166, no. 9, pp. B3184–B3193, 2019, doi: 10.1149/2.0241907jes. [Online]. Available: https://iopscience.iop.org/article/10.1149/2.0241907jes/pdf
[17] C. Belkhiria, A. Boudir, C. Hurter, and V. Peysakhovich, “EOG-Based Human–Computer Interface: 2000–2020 Review,” pp. 1–19, 2022, doi: 10.3390/s22134914. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269776/pdf/sensors-22-04914.pdf
[18] I. Muchlis, R. Maulana, and H. Fitriyah, “Implementasi Pengenalan Pergerakan Bola Mata Menggunakan Elektroda Dengan Exponential Filter,” Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 9, pp. 3093–3102, 2018. [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/2545/941
Published
2025-04-30
How to Cite
[1]
H. Rachmat, “Design and Implementation of an Arduino Nano-Based Pinky Finger EMG and Eye EOG Physiological Signal Generator”, JME, Apr. 2025.
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Copyright (c) 2025 Hendi Handian Rachmat
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