PENGENALAN GERAK JARI TANGAN BERDASARKAN SINYAL ELECTROMYOGRAPHY (EMG) MENGGUNAKAN LEARNING VECTOR QUANTIZATION (LVQ)

Main Article Content

Darma Setiawan Putra

Abstract

Sinyal EMG merupakan sinyal elektrik yang timbul pada lapisan otot manusia. Sinyal ini muncul disebabkan oleh adanya aktifitas kontraksi pada otot. Sinyal EMG ini dapat diakuisisi dengan menggunakan sebuah elektroda yang ditempelkan pada lapisan permukaan kulit. Jumlah subyek penelitian yang dilibatkan dalam studi ini sebanyak 6 orang dimana masing-masing subyek memiliki 10 dataset sinyal EMG. Sinyal mentah (raw) EMG akan dinormalisasi terlebih dahulu dengan menggunakan metoda zero-mean. Setelah dilakukan proses normalisasi, sinyal EMG akan difilter, rectified dan smoothing untuk menghasilkan envelope sinyal EMG. Dari envelope sinyal EMG tersebut peroleh5 buah fitur sinyal berupa onset, offset, durasi, nilai maksimum dan nilai minimum. Penelitian ini menggunakan metode learning vector quantization (LVQ) untuk pengenalan sinyal EMG di otot tangan pada saat gerakan fleksi pergelangan tangan yang dikombinasikan dengan jari terbuka (JBF). Dari penelitian ini menunjukkan akurasi pengklasifikasian untuk data gerakan JBF sebesar 61.66%.Dengan hasil ini menunjukkan bahwa sinyal EMG pada otot tangan dapat digunakan untuk identitas biometrik dan pengontrolan tangan buatan

Downloads

Download data is not yet available.

Article Details

Section
Articles

References

[1] R. S. Razavian, S. Greenberg, and J. McPhee, “Biomechanics Imaging and Analysis,” R. B. T.-E. of B. E. Narayan, Ed. Oxford: Elsevier, 2019, pp. 488–500.
[2] N. Duan, L.-Z. Liu, X.-J. Yu, Q. Li, and S.-C. Yeh, “Classification of multichannel surface-electromyography signals based on convolutional neural networks,” J. Ind. Inf. Integr., 2018.
[3] D. S. Putra, A. D. Wibawa, and M. H. Purnomo, “Classification of EMG during Walking using Principal Component Analysis and Learning Vector Quantization,” in International Seminar on Intelligent Technology and Its Application, 2016, pp. 145–150.
[4] O. W. Samuel, H. Zhou, X. Li, H. Wang, H. Zhang, A. K. Sangaiah, and G. Li, “Pattern recognition of electromyography signals based on novel time domain features for amputees’ limb motion classification,” Comput. Electr. Eng., vol. 67, pp. 646–655, 2018.
[5] C. L. Lipinski, L. Donovan, T. J. McLoughlin, C. W. Armstrong, and G. E. Norte, “Surface electromyography of the forearm musculature during an overhead throwing rehabilitation progression program,” Phys. Ther. Sport, vol. 33, pp. 109–116, 2018.
[6] R. N. Khushaba, S. Kodagoda, M. Takruri, and G. Dissanayake, “Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals,” Expert Syst. Appl., vol. Vol. 39, no. 12, pp. 10731–10738, 2012.
[7] D. K. Kumar, S. P. Arjunan, and V. P. Singh, “Towards identification of finger flexions using single channel surface electromyography – able bodied and amputee subjects,” J. Neuroeng. Rehabil., pp. 1–7, 2013.
[8] A. H. Al-Timemy, G. Bugmann, J. Escudero, and N. Outram, “Classification of Finger Movements for the Dexterous Hand Prosthesis Control With Surface Electromyography,” J. Biomed. Heal. Informatics, vol. Vol. 17, no. 3, pp. 608–618, 2013.
[9] M. Irfan, W. Caesarendra, and M. Ariyanto, “Studi Klasifikasi Tujuh Gerakan Tangan Sinyal Electromyography (EMG) Menggunakan Metode Pattern Recognition,” J. Tek. Mesin Undip, vol. Vol. 4, no. 3, pp. 307–316, 2016.
[10] O. Barzilay and A. Wolf, “A fast implementation for EMG signal linear envelope computation,” J. Electromyogr. Kinesiol., vol. 21, no. 4, pp. 678–682, 2011.
[11] B. Setyonugroho, A. E. Permanasari, and S. S. Kusumawardani, “Perbandingan Akurasi Algoritme Pelatihan dalam Jaringan Syaraf Tiruan untuk Peramalan Jumlah Pengguna Kereta Api di Pulau Jawa,” J. Metik, vol. 1, no. 1, 2017.
[12] R. Ahsan, M. I. Ibrahimy, and O. O. Khalifa, “Electromygraphy ( EMG ) Signal based Hand Gesture Recognition using Artificial Neural Network ( ANN ),” no. May, pp. 17–19, 2011.
[13] Y. L. Chong and K. Sundaraj, “A study of back-propagation and radial basis neural network on EMG signal classification,” 2009 6th Int. Symp. Mechatronics its Appl. ISMA 2009, pp. 1–6, 2009.
[14] T. Kohonen, “Self-Organizing Maps,” Third Edit., Springer, 2000.
[15] T. Villmann, M. Kaden, M. Lange, P. Sturmer, and W. Hermann, “Precision-recall-optimization in learning vector quantization classifiers for improved medical classification systems,” IEEE SSCI 2014 - 2014 IEEE Symp. Ser. Comput. Intell. - CIDM 2014 2014 IEEE Symp. Comput. Intell. Data Mining, Proc., pp. 71–77, 2015.
[16] D. M. W. Powers, “Evaluation : From Precision , Recall and F-Factor to ROC , Informedness , Markedness & Correlation,” no. December, 2007.