Classification of Emg Signals Using Convolution Neural Network

Yıl 2020, , 115 - 119, 31.12.2020

Kaan Bakırcıoğlu , Nalan Özkurt

https://doi.org/10.18100/ijamec.795227

Cited By: 5

Öz

An electrical signal is produced by the contraction of the muscles; this electrical signal contains information about the muscles, the recording of these signals called electromyography (EMG). This information is often used in studies such as prosthetic arm, muscle damage detection, and motion detection. Classifiers such as artificial neural networks, support vector machines are generally used for the classification of EMG signals. Despite successful results with such methods the extraction of the features to be given to the classifiers and the selection of the features affect the classification success. In this study, it is aimed to increase the success of the classification of the daily used hand movements using the Convolutional neural networks (CNN). The advantage of the deep learning techniques like CNN is that the relationships in big data are learned by the network. Firstly, the received EMG signals for forearms are windowed to increase the number of data and focus on the contraction points. Then, to compare the success rate, raw signals, Fourier transform of the signal, the root means square, and the Empirical mode decomposition (EMD) is applied to the signal and intrinsic mode functions are obtained. These signals are given to four different CNN. Afterward, to find the most efficient parameters, the results were obtained by splitting data set into three as 70% training set, 15% validation set, and 15% test set. 5 cross-validations have been applied to assess the system’s performance. The best results are obtained from the CNN, which receive the EMD applied signal as input. The result obtained with the cross-validation is 95.90% and the result obtained with the other separation method is 93.70%. When the results were examined, it was seen that CNN is a promising classifier even the raw signal is applied to the classifier. Also, it has been observed that EMD method creates better accuracy of classification.


This is an open access article under the CC BY-SA 4.0 license.
(https://creativecommons.org/licenses/by-sa/4.0/)

Anahtar Kelimeler

Electromyography, Machine Learning, Convolutional Neural Network, Empirical Mode, Fourier Transform

Kaynakça

• A. G. DiGiovanna, Human Aging: Biological Perspectives. The McGraw-Hill Companies, New York, 2000.
• J. R. Cram, “The history of surface electromyography,” Applied Psychophysiology Biofeedback, vol. 28, no. 2. Springer, pp. 81–91, Jun. 2003, doi: 10.1023/A:1023802407132.
• M. F. Lucas, A. Gaufriau, S. Pascual, C. Doncarli, and D. Farina, “Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization,” Biomed. Signal Process.,2008.
• D. Bağcı, “Biyonik El Kontrolü İçin Emg İşaretlerinin Makine Öğrenmesi Yöntemiyle Siniflandirilmasi,” Yalova Üniversitesi, 2016.
• R. J. Oweis, R. Rihani, and A. Alkhawaja, “ANN-based EMG classification for myoelectric control,” Int. J. Med. Eng. Inform., vol. 6, no. 4, pp. 365–380, Oct. 2014, doi: 10.1504/IJMEI.2014.065442.
• F. Ayaz, “Emg Sinyallerinin Siniflandirilmasi,” İnönü Üniversitesi, 2018.
• R. E. Neapolitan, Neural Networks and Deep Learning. 2018.
• M. A. Oskoei and H. Hu, “Support vector machine-based classification scheme for myoelectric control applied to upper limb,” IEEE Trans. Biomed. Eng.,2008.
• E. Podrug and A. Subasi, “Surface EMG pattern recognition by using DWT feature extraction and SVM classifier,” 1st Conf. Med. Biol. Eng. Bosnia Herzegovina, no. March, pp. 1–3, 2015, [Online].
• L. Wei and H. Hu, “EMG and visual based HMI for hands-free control of an intelligent wheelchair,” in Proceedings of the World Congress on Intelligent Control and Automation (WCICA), 2010, pp. 1027–1032, doi: 10.1109/WCICA.2010.5554766.
• N. Rabin, M. Kahlon, S. Malayev, and A. Ratnovsky, “Classification of human hand movements based on EMG signals using nonlinear dimensionality reduction and data fusion techniques,”,2020
• H. P. Huang and C. Y. Chen, “Development of a myoelectric discrimination system for a multi-degree prosthetic hand,”, 1999, doi: 10.1109/robot.1999.770463.
• U. Başpınar, “Elektromi̇yogram si̇nyalleri̇ni̇n siniflandirilmasi ve bağimsiz bi̇leşen anali̇zi̇ i̇le i̇şlenmesi̇,” 2014.
• M. Z. Rehman et al., “Multiday EMG-Based classification of hand motions with deep learning techniques,” Sensors (Switzerland), vol. 18, no. 8, Aug. 2018.
• C. Sapsanis, G. Georgoulas, A. Tzes, and D. Lymberopoulos, “Improving EMG based classification of basic hand movements using EMD,” 2013, doi: 10.1109/EMBC.2013.6610858.
• D. A. Eroğlu, “Real Time Elbow Joint Angle Estimation Using Semg Signals,”, 2013.
• V. Bajaj and R. B. Pachori, “EEG signal classification using empirical mode decomposition and support vector machine,”, 2012, doi: 10.1007/978-81-322-0491-6.
• A. C. Ian Goodfellow, Yoshua Bengio, Deep learning, vol. 12, no. 8. 2018.
• E. S. Ghrairi, “Konvolüsyonel Sinir Ağlari Kullanilarak Çiçek Türlerinin Siniflandirilmasi,” Selçuk Üniversitesi, 2019.