Pengenalan Pola Sinyal Electromyography (EMG) pada Gerakan Jari Tangan Kanan

WAHYU MULDAYANI, ARIZAL MUJIBTAMALA NANDA IMRON, KHAIRUL ANAM, SUMARDI SUMARDI, WIDJONARKO WIDJONARKO, ZILVANHISNA EMKA FITRI

Sari


ABSTRAK

Sinyal EMG merupakan salah satu sinyal yang dapat digunakan untuk memberikan perintah pada kursi roda listrik. Sinyal EMG yang digunakan diambil dari sinyal otot fleksor dan ekstensor yang berada di tangan kanan. Sinyal tersebut diambil menggunakan sensor Myo Armband. Klasifikasi sinyal EMG diambil dari pergerakan jari yang mewakili perintah gerak yaitu jari kelingking untuk bergerak maju, jari manis untuk berhenti, jari tengah untuk belok kanan dan jari telunjuk untuk belok kiri. Setiap sinyal EMG diekstraksi fitur untuk menentukan karakteristik sinyal sehingga fitur yang diperoleh adalah Average Absolute Value, Root Mean Square, Simple Integral Square, EMG Simple Variant and Integrated EMG. Kemudian fitur tersebut digunakan sebagai input dari metode klasifikasi Artificial Neural Network Backpropagation. Jumlah data latih yang digunakan adalah 800 data sedangkan data uji yang digunakan adalah 200 data. Tingkat keberhasilan proses klasifikasi ini sebesar 93%.

Kata kunci: electromyogram, artificial neural network, klasifikasi sinyal, tangan kanan, Myo Armband.

 

ABSTRACT

EMG signal is one of the signals that can be used to give orders to electric wheelchairs. The EMG signal used is taken from the flexor and extensor muscle signals in the right hand. The signal is taken using the Myo Armband sensor. The EMG signal classification is taken from the movement of the finger which represents the command of motion ie the little finger to move forward, ring finger to stop, middle finger to turn right and index finger to turn left. Each EMG signal is extracted features to determine the signal characteristics so that the features obtained are Average Absolute Value, Root Mean Square, Simple Integral Square, EMG Simple Variant and Integrated EMG. Then the feature is used as input from the Backpropagation classification method. The amount of training data used is 800 data while the test data used is 200 data. The success rate of this classification process is 93%.

Keywords: electromyogram, artificial neural network, signal classification, right hand, Myo Armband.


Kata Kunci


Sinyal EMG; Myo Armband; Neural Network Backpropagation; Kursi Roda Elektrik

Teks Lengkap:

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v8i3.591

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ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638

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Department of Electrical Engineering Institut Teknologi Nasional Bandung

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Contact: +627272215 (ext. 206)

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