Klasifikasi Gerakan Jari Tangan Berdasarkan Sinyal Electromyogram Pada Lengan

https://doi.org/10.22146/ijeis.60741

Catur Atmaji(1*), Yusuf Waraqa Santoso(2), Roghib Muhammad Hujja(3), Andi Dharmawan(4), Danang Lelono(5)

(1) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(2) Prodi Elektronika dan Instrumentasi, DIKE, FMIPA UGM, Yogyakarta
(3) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(4) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(5) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(*) Corresponding Author

Abstract


An electromyogram is a recording of muscle activity. These signals have been used both for medical diagnosis and engineering such as finger motion detection in healthy people and rehabilitation patients. Many studies have been conducted to map the relationship between electromyogram and finger movements, one of which is the relationship between the number of channels used and the complexity of the system. The number of channels used is directly proportional to the complexity of a system. The more complex the system, the heavier the data processing is so that it requires greater resources. Therefore, this study focuses on the construction of a classification system for human finger movements using fewer channels. The number of channels used in this study is 4. Root Mean Square is applied in a sliding window as feature extraction. The classifier used is the artificial neural network. System validation is done with 10-fold cross-validation. The test results of the average accuracy value for the thumb, index finger, middle finger, ring finger, little finger, grip, and relaxation were 89%, 90%, 93%, 95%, 93%, 94%, and 91% respectively which can be said to be quite good considering the number of channels relatively few compared to previous studies.

Keywords


artificial neural network; root mean square; 4-channels EMG

Full Text:

PDF


References

[1] A. Junlasat, T. Kamolklang, P. Uthansakul, and M. Uthansakul, “Finger Movement Detection Based on Multiple EMG Positions,” 2019 11th International Conference on Information Technology and Electrical Engineering, ICITEE 2019, vol. 7, pp. 7–10, 2019, doi: 10.1109/ICITEED.2019.8929980.

[2] M. Barsotti, S. Dupan, I. Vujaklija, S. Došen, A. Frisoli, and D. Farina, “Online Finger Control Using High-Density EMG and Minimal Training Data for Robotic Applications,” IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 217–223, 2019, doi: 10.1109/LRA.2018.2885753.

[3] I. Yeo and H. C. Shin, “Novel Korean finger language recognition using EMG and motion sensors,” International Conference on Information Networking, vol. 2018-Janua, pp. 837–839, 2018, doi: 10.1109/ICOIN.2018.8343238.

[4] K. Rhee and H. C. Shin, “Finger motion recognition robust to diverse arm postures using EMG and accelerometer,” International Conference on Information Networking, vol. 2018-Janua, pp. 834–836, 2018, doi: 10.1109/ICOIN.2018.8343237.

[5] T. Hiyama, S. Sakurazawa, M. Toda, J. Akita, K. Kondo, and Y. Nakamura, “Motion estimation of five fingers using small concentric ring electrodes for measuring surface electromyography,” 2014 IEEE 3rd Global Conference on Consumer Electronics, GCCE 2014, no. 2001, pp. 376–380, 2015, doi: 10.1109/GCCE.2014.7031268.

[6] F. Zhang, L. Lin, L. Yang, and Y. Fu, “Design of an active and passive control system of hand exoskeleton for rehabilitation,” Applied Sciences (Switzerland), vol. 9, no. 11, 2019, doi: 10.3390/app9112291.

[7] N. Naseer, F. Ali, S. Ahmed, S. Iftikhar, R. A. Khan, and H. Nazeer, “EMG Based Control of Individual Fingers of Robotic Hand,” 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings, pp. 6–9, 2018, doi: 10.1109/SIET.2018.8693177.

[8] N. Nazmi, M. A. A. Rahman, S. I. Yamamoto, S. A. Ahmad, H. Zamzuri, and S. A. Mazlan, “A review of classification techniques of EMG signals during isotonic and isometric contractions,” Sensors (Switzerland), vol. 16, no. 8, pp. 1–28, 2016, doi: 10.3390/s16081304.

[9] M. Li et al., “An attention-controlled hand exoskeleton for the rehabilitation of finger extension and flexion using a rigid-soft combined mechanism,” Frontiers in Neurorobotics, vol. 13, no. May, pp. 1–13, 2019, doi: 10.3389/fnbot.2019.00034.

[10] C. Dai and X. Hu, “Finger Joint Angle Estimation Based on Motoneuron Discharge Activities,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 3, pp. 760–767, 2020, doi: 10.1109/JBHI.2019.2926307.

[11] S. Stapornchaisit, Y. Kim, A. Takagi, N. Yoshimura, and Y. Koike, “Finger angle estimation from array EMG system using linear regression model with independent component analysis,” Frontiers in Neurorobotics, vol. 13, no. September, pp. 1–12, 2019, doi: 10.3389/fnbot.2019.00075.

[12] X. Li, J. Fu, L. Xiong, Y. Shi, R. Davoodi, and Y. Li, “Identification of finger force and motion from forearm surface electromyography,” IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, vol. 2015-Octob, pp. 316–321, 2015, doi: 10.1109/MFI.2015.7295827.

[13] A. H. Al-timemy, S. Member, G. Bugmann, J. Escudero, and N. Outram, “Classification of Finger Movements for the Dexterous Hand Prosthesis Control With Surface Electromyography,” IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 3, pp. 608–618, 2013, doi: 10.1109/JBHI.2013.2249590.

[14] C. Atmaji and Z. Y. Perwira, “Pengaruh Latar Belakang Warna pada Objek Gambar terhadap Hasil Ekstraksi Sinyal EEG,” Indonesian Journal of Electronics and Instrumentation System, vol. 7, no. 2, pp. 161–172, 2017.

[15] P. Liu, D. R. Brown, E. A. Clancy, F. Martel, and D. Rancourt, “EMG-force estimation for multiple fingers,” 2013 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2013, 2013, doi: 10.1109/SPMB.2013.6736772.



DOI: https://doi.org/10.22146/ijeis.60741

Article Metrics

Abstract views : 2107 | views : 1878

Refbacks

  • There are currently no refbacks.




Copyright (c) 2021 IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



Copyright of :
IJEIS (Indonesian Journal of Electronics and Instrumentations Systems)
ISSN 2088-3714 (print); ISSN 2460-7681 (online)
is a scientific journal the results of Electronics
and Instrumentations Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijeis.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijeis



View My Stats1
View My Stats2