Year 2010,
Volume: 13 Issue: 2, 63 - 80, 05.06.2016
Abstract
In this study, EMG signals taken from the skin surface as a result of muscles' contraction are classified.
Studied EMG signals include 400 different patterns relating to four different movements. Each pattern is obtained
by adding EMG signals one after another, which are recorded synchronously from two different muscles relating to
one movement. Support Vector Machine (SVM) classifier, a supervised method, is used to classify these pattterns.
But signals need to be preprocessed before being used in SVM classifier. To this end, spectral methods are
consulted. In this way, feature vectors which are more significant than raw data and are composed of coefficients are
achieved. Four different methods are used for preprocessing and feature vectors obtained are classified by SVM.
Success of SVM classifier is tested and performances of preprocessing methods are compared. Best achievement is
94.25%.
Keywords: EMG; Spectral Methods; Autoregressive (AR); SVM Classifier.
References
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- Uygulamaları Kurultayı (SİU’05) Kayseri, Türkiye.
- Asres A., Dou H., Zhou Z., Zhang Y. and Zhu S., “A combination of AR and neural network technique for EMG pattern identification.”, 18th Annual International Conference of the IEEE Engineering in Medicine And Biology Society, 1996, Amsterdam, 1464-1465.
- Bronzino J. D. 1996., The Biomedical Engineering handbook, IEEE Pres,3erd edition.
- Chan F.H.Y., Yang Y.S.Y., Lam F.K., Zhang Y.T., Parker P.A. 2000., Fuzzy EMG Classification for Prosthesis
- Rehabilitation Engineering, 8(3):305-311. IEEE Transactions
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- Huang H.P., Liu Y.H., Liu L.W., Wong C.S. 2003., EMG Classification for Prehensile Postures Using Cascaded Architecture of Neural Networks with Self- organizing Maps. lnternational Conference on Robotics & Automation, 1497-1502.
- Hudgins B., Parker P.A. and Scott R.N. 1993., A new strategy for multifunction myoelectric control, IEEE Transactions on Biomedical Engineering, 40, 1, 82-94.
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- Karlık, B. 1994., Çok Fonksiyonlu Protezler İçin Yapay Sinir Ağları Kullanılarak Miyoelektrik Kontrol. Yıldız Teknik Üniversitesi, Fen Bil. Enstitüsü Doktora Tezi.
- Khezri M., Jahed M., Sadati N. 2007., Neuro-Fuzzy Surface EMG Pattern Recognition for Multifunctional Hand Prosthesis Control. IEEE, 269-274.
- Kocyiğit Y., Korürek M. 2005., EMG İşaretlerini Dalgacık Dönüşümü ve Bulanık Mantık Sınıflayıcı Kullanarak 4(3):25-31.
- Lucas M.F., Gaufriau A., Pascual S., Doncarli C., Farina D. 2008., Multi-Channel Surface EMG Classification Using Support Vector Machines and Signal-Based Wavelet Optimization Machines and Signal-Based Wavelet Optimization. Biomedical Signal Processing and Control, 3:169-174.
- Mühendislik, 20.Oskoei M.A., Hu H. 2007., Myoelectric Control Systems. Biomedical Signal Processing and Control, 2:275-294.
- Oskoei M.A., Hu H. 2008., Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb, IEEE Transactions on Biomedical Engineering, Vol. 55, No. 8
- Proakis J.G., Manolakis D.G. 1996., Digital Signal Processing Principles, Algorithms, and Applications. Prentice-Hall, New Jersey.
- Qian H., Mao Y., Xiang W., Wang Z. 2010., Recognition of human activities using SVM multi-class classifier, Pattern Recognition Letters,31,100–111.
- Subasi A., Yilmaz M., Ozcalik H.R. 2006., Classification of EMG Signals Using Wavelet Neural Network. Journal of Neuroscience Methods, 156:360- 367.
- Şeker M., Tokmakçı M., Asyalı M.H., Seğmen H. 2010., Gebelik Sürecindeki Migrenli Hastalarda EEG Sinyallerinin Parametrik ve Parametrik Olmayan Spektral Analiz yöntemleri ile İncelenmesi, Biyomut 2010, Antalya, Turkey.
- Ubeyli E. D., Guler I. 2004., Selection of optimal AR spectral estimation method for internal carotid arterial Doppler signals using Cramer-Rao bound. Comput. Electr. Eng. 30:491–508.
- Wang A., Yuan W., Liu J., Yu Z., Li H. 2009., A novel pattern recognition algorithm: Combining ART network with SVM to reconstruct a multi-class classifier,
- Applications ,57 ,1908-1914. and Mathematics with
Year 2010,
Volume: 13 Issue: 2, 63 - 80, 05.06.2016
Abstract
Bu çalışmada kas liflerinin kasılması neticesinde deri yüzeyinden algılanan elektromiyografi (EMG) işaretleri sınıflandırılmıştır. Çalışılan EMG işareti dört farklı harekete ait toplam 400 farklı örüntüden oluşmaktadır. Her bir örüntü bir harekete ait iki farklı kastan eş zamanlı olarak kaydedilen EMG işaretinin art arda eklenmesiyle elde edilmiştir. Bu örüntülerin sınıflandırılması için danışmanlı bir yöntem olan Destek Vektör Makinesi (DVM) sınıflandırıcı kullanılmıştır. Fakat DVM sınıflandırıcı kullanılmadan önce işaretin bir ön işlemeden geçmesi gerekmektedir. Bu amaçla da spektral yöntemlere başvurulmuştur. Böylece işlenmemiş veriden daha anlamlı ve katsayılardan oluşan özellik vektörleri elde edilmiştir. Ön işleme için dört farklı yöntem kullanılmış, elde edilen özellik vektörleri de DVM sınıflandırıcı ile sınıflara ayrılmıştır. Kullanılan veri seti için DVM sınıflandırıcının başarısı ölçülmüş ve ön işleme metotlarının performansı kıyaslanmıştır. En yüksek başarı oranı %94,25’tir
References
- Ahmad S.A., Chappell P.H. 2007., Surface EMG Classification Using Moving Approximate Entropy. International Conference on Intelligent and Advanced Systems 2007, 1163-1167. 2.
- Alkan A., Gunay M. 2010., Identification of EMG signals using discriminant analysis and SVM classifier, ISCSE 2010, Aydın/Kuşadası. 3.
- Alkan A., Kiymik M.K. 2006., Comparison of AR andWelch Methods in Epileptic Seizure Detection J Med Syst 30:413–419, DOI 10.1007/s10916-005-9001- 0.
- Alkan A., Subaşı A., Kıymık M.K. 2005., Epilepsi Tanısında MUSIC
- Karşılaştırılması, IEEE 13. Sinyal İşleme ve İletişim ve
- Uygulamaları Kurultayı (SİU’05) Kayseri, Türkiye.
- Asres A., Dou H., Zhou Z., Zhang Y. and Zhu S., “A combination of AR and neural network technique for EMG pattern identification.”, 18th Annual International Conference of the IEEE Engineering in Medicine And Biology Society, 1996, Amsterdam, 1464-1465.
- Bronzino J. D. 1996., The Biomedical Engineering handbook, IEEE Pres,3erd edition.
- Chan F.H.Y., Yang Y.S.Y., Lam F.K., Zhang Y.T., Parker P.A. 2000., Fuzzy EMG Classification for Prosthesis
- Rehabilitation Engineering, 8(3):305-311. IEEE Transactions
- On 8.Doerschuk P.C., Gustafson D.E. and Willsky A.S. 1983, Upper extremity limb function discrimination using EMG signal analysis, IEEE Transactions on Biomedical Engineering, 30, 1,18-29.
- Englehart K., Hudgins B., Parker P.A. 1999., Stevenson M., Classification of the myoelectric signal using time-frequency based representations. Med Eng Phys., 21:431–438.
- Fidan, C.B. 2001. Dirsek Üstü Kol Protezinin YSA Kullanılarak DSP Tabanlı Bir Devre ile Gerçek Zamanda Kontrolü. Yıldız Teknik Üniversitesi, Fen Bil. Enstitüsü Doktora Tezi.
- Graupe D., Salahi, J. and Zhang, D. 1985., Stochastic analysis of myoelectric temporal signatures for multifunction single-site activation of prostheses and orthoses, Journal of Biomedical Engineering., 7, 1, 18- 29, 1985.
- Huang H.P., Liu Y.H., Liu L.W., Wong C.S. 2003., EMG Classification for Prehensile Postures Using Cascaded Architecture of Neural Networks with Self- organizing Maps. lnternational Conference on Robotics & Automation, 1497-1502.
- Hudgins B., Parker P.A. and Scott R.N. 1993., A new strategy for multifunction myoelectric control, IEEE Transactions on Biomedical Engineering, 40, 1, 82-94.
- Kang W., Shiu J., Cheng C., Lai L., Tsao H. and Kuo T. 1995., The application of cepstral coefficients and maximum likelihood method in EMG pattern recognition,
- Engineering, 42, 777-785. on
- Biomedical 15.Karlık B., Tokhi O., Alcı M. A. 2003., Fuzzy Clustering Neural Network Architecture for Multi- Function Upper-Limb Prosthesis, IEEE Transactions on Biomedical Engineering, 50, 11, 1255-1261.
- Karlık, B. 1994., Çok Fonksiyonlu Protezler İçin Yapay Sinir Ağları Kullanılarak Miyoelektrik Kontrol. Yıldız Teknik Üniversitesi, Fen Bil. Enstitüsü Doktora Tezi.
- Khezri M., Jahed M., Sadati N. 2007., Neuro-Fuzzy Surface EMG Pattern Recognition for Multifunctional Hand Prosthesis Control. IEEE, 269-274.
- Kocyiğit Y., Korürek M. 2005., EMG İşaretlerini Dalgacık Dönüşümü ve Bulanık Mantık Sınıflayıcı Kullanarak 4(3):25-31.
- Lucas M.F., Gaufriau A., Pascual S., Doncarli C., Farina D. 2008., Multi-Channel Surface EMG Classification Using Support Vector Machines and Signal-Based Wavelet Optimization Machines and Signal-Based Wavelet Optimization. Biomedical Signal Processing and Control, 3:169-174.
- Mühendislik, 20.Oskoei M.A., Hu H. 2007., Myoelectric Control Systems. Biomedical Signal Processing and Control, 2:275-294.
- Oskoei M.A., Hu H. 2008., Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb, IEEE Transactions on Biomedical Engineering, Vol. 55, No. 8
- Proakis J.G., Manolakis D.G. 1996., Digital Signal Processing Principles, Algorithms, and Applications. Prentice-Hall, New Jersey.
- Qian H., Mao Y., Xiang W., Wang Z. 2010., Recognition of human activities using SVM multi-class classifier, Pattern Recognition Letters,31,100–111.
- Subasi A., Yilmaz M., Ozcalik H.R. 2006., Classification of EMG Signals Using Wavelet Neural Network. Journal of Neuroscience Methods, 156:360- 367.
- Şeker M., Tokmakçı M., Asyalı M.H., Seğmen H. 2010., Gebelik Sürecindeki Migrenli Hastalarda EEG Sinyallerinin Parametrik ve Parametrik Olmayan Spektral Analiz yöntemleri ile İncelenmesi, Biyomut 2010, Antalya, Turkey.
- Ubeyli E. D., Guler I. 2004., Selection of optimal AR spectral estimation method for internal carotid arterial Doppler signals using Cramer-Rao bound. Comput. Electr. Eng. 30:491–508.
- Wang A., Yuan W., Liu J., Yu Z., Li H. 2009., A novel pattern recognition algorithm: Combining ART network with SVM to reconstruct a multi-class classifier,
- Applications ,57 ,1908-1914. and Mathematics with
There are 32 citations in total.
Details
| Primary Language | English |
|---|---|
| Journal Section | Research Articles |
| Authors | |
| Publication Date | June 5, 2016 |
| Submission Date | January 25, 2011 |
| Published in Issue | Year 2010Volume: 13 Issue: 2 |