An Ensemble Classifier for Finger Movement Recognition using EMG Signals

Year 2019, Volume: 7 Issue: 4, 96 - 99, 31.12.2019

İlker Ali Özkan

https://doi.org/10.18100/ijamec.659781

Cited By: 2

Abstract

Electromyography
(EMG) signals that obtained by electrodes connected to the forearm are the
monitoring of the muscles by the electrical method. These signals are quite
useful during the use of prosthesis as a source signal to the moving prosthesis.
Therefore, it is essential that classifying the EMG signals with high accuracy
by analyzing. This study aims that classifying the individual and combined
finger movements using surface EMG signals taken from the surface of the human
forearm. EMG signals that belong to 10 different finger movements obtained from
eight subjects were used. Firstly, EMG signals have been split into segments by
the windowing process, and temporal feature vectors are formed by applying
various feature extraction methods to these segments.  Feature vectors have been classified with the
ensemble bagged tree algorithm, which is a combination of classifiers, to
obtain the correct classification decision. As a result of 10-fold
cross-validation, with the proposed method, 96.6% overall classification
accuracy was achieved. The results obtained show that the ensemble classifier
can be used successfully in determining finger movements when compared with
similar studies.

Keywords

Ensemble Classifier, Bagged Tree, Electromyography, Feature Extraction, Finger Movement

References

• J. D. Bronzino and D. R. Peterson, Biomedical engineering fundamentals. CRC press, 2014.
• Altınöz Şakir, Ç. Süleyman, Ü. Osman, and K. Erkan, “Design of EMG Based Classification for 5-axis Robot Arm Control,” in 2016 yılı Otomatik Kontrol Ulusal Toplantısı (TOK’2016), 2016, pp. 271–275.
• M. B. I. Reaz, M. S. Hussain, and F. Mohd-Yasin, “Techniques of EMG signal analysis: detection, processing, classification and applications (Correction),” Biol. Proced. Online, vol. 8, no. 1, p. 163, 2006.
• J. Kimura, Electrodiagnosis in diseases of nerve and muscle: principles and practice. Oxford university press, 2013.
• E. Criswell, Cram’s introduction to surface electromyography. Jones & Bartlett Publishers, 2010.
• F. Hardalac and M. Poyraz, “Classification of EMG Signals Using Artificial Neural Network and Diagnosis of Neuropathy Neuromuscular Disease,” J. Polytech., vol. 5, no. 1, pp. 75–83, Mar. 2002.
• J. J. Carr and J. M. Brown, Introduction to biomedical equipment technology. Prentice hall, 2001.
• P. Polygerinos, K. C. Galloway, S. Sanan, M. Herman, and C. J. Walsh, “EMG controlled soft robotic glove for assistance during activities of daily living,” in 2015 IEEE international conference on rehabilitation robotics (ICORR), 2015, pp. 55–60.
• L. R. Quitadamo et al., “Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review,” J. Neural Eng., vol. 14, no. 1, p. 11001, 2017.
• 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. Control, vol. 3, no. 2, pp. 169–174, Apr. 2008.
• M. Khezri, M. Jahed, and N. Sadati, “Neuro-fuzzy surface EMG pattern recognition for multifunctional hand prosthesis control,” in IEEE International Symposium on Industrial Electronics, 2007, pp. 269–274.
• C. Cerci and H. Temeltas, “Feature extraction of EMG signals, classification with ANN and kNN algorithms,” in 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, 2018.
• Z.-H. Zhou, Ensemble methods: foundations and algorithms. Chapman and Hall/CRC, 2012.
• R. Polikar, “Ensemble based systems in decision making,” IEEE Circuits Syst. Mag., vol. 6, no. 3, pp. 21–45, 2006.
• 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. 39, no. 12, pp. 10731–10738, 2012.
• M. Koklu, K. Sabanci, “Estimation of Credit Card Customers Payment Status by Using kNN and MLP,” International Journal of Intelligent Systems and Applications in Engineering, pp. 249–251, 2016.
• K. Englehart and B. Hudgins, “A robust, real-time control scheme for multifunction myoelectric control,” IEEE Trans. Biomed. Eng., vol. 50, no. 7, pp. 848–854, 2003.
• A. Islam and M. S. Alam, “Classification of Electromyography Signals Using Support Vector Machine,” 2017.
• L. H. Smith, L. J. Hargrove, B. A. Lock, and T. A. Kuiken, “Determining the Optimal Window Length for Pattern Recognition-Based Myoelectric Control: Balancing the Competing Effects of Classification Error and Controller Delay,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 19, no. 2, pp. 186–192, 2011.
• N. Nazmi et al., “Assessment on stationarity of EMG signals with different windows size during isotonic contractions,” Appl. Sci., vol. 7, no. 10, p. 1050, 2017.
• A. Phinyomark, P. Phukpattaranont, and C. Limsakul, “Feature reduction and selection for EMG signal classification,” Expert Syst. Appl., vol. 39, no. 8, pp. 7420–7431, 2012.
• T. Triwiyanto, O. Wahyunggoro, H. A. Nugroho, and H. Herianto, “An investigation into time domain features of surface electromyography to estimate the elbow joint angle,” Adv. Electr. Electron. Eng., vol. 15, no. 3, pp. 448–458, 2017.
• A. Mert, N. Kilic, and A. Akan, “ECG signal classification using ensemble decision tree,” J Trends Dev Mach Assoc Technol, vol. 16, no. 1, pp. 179–182, 2012.
• S. Tasdemir, I. Saritas,M. Ciniviz and N. Allanhverdi, “Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine,” Expert Systems with Applications, vol. 38, no. 11, pp. 13912-13923, 2011.