Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition

Electromyography (EMG) signal is a biomedical signal which measures physical activity of human muscle.It has been acknowledged to be widely used in rehabilitation or recovery application system assisting physiotherapist to monitor a patient’s physical strength,function,motion and overall well-being...

Full description

Saved in:

Bibliographic Details
Main Author:	Burhan, Nuradebah
Format:	Thesis
Language:	English English
Published:	2018
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/23299/1/Spectral%20Estimation%20And%20Supervised%20Classification%20Technique%20For%20Real%20Time%20Electromyography%20Pattern%20Recognition.pdf http://eprints.utem.edu.my/id/eprint/23299/2/Spectral%20Estimation%20And%20Supervised%20Classification%20Technique%20For%20Real%20Time%20Electromyography%20Pattern%20Recognition.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utem-ep.23299
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Burhan, Nuradebah Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition
description	Electromyography (EMG) signal is a biomedical signal which measures physical activity of human muscle.It has been acknowledged to be widely used in rehabilitation or recovery application system assisting physiotherapist to monitor a patient’s physical strength,function,motion and overall well-being by addressing the underlying physical issues.In application system associated with rehabilitation,a signal processing and classification techniques are implemented to classify EMG signal obtained.For real time application in the rehabilitation, the classification is crucial issue.The success of the signal classification depends on the selection of the features that represent a raw EMG signal in the signal processing.Therefore,a robust and resilient denoising method and spectral estimation technique have been acknowledged as necessary to distinguish and detect the EMG pattern.The present study was undertaken to determine the characteristic of EMG features using denoising method and spectral estimation technique for assessing the EMG pattern based on a supervised classification algorithm.In the study,the combination of time-frequency domain (TFD) and time domain (TD) were identified as the preferred denoising method and spectral estimation techniques.In the first part of study, the recorded EMG signal filtered the contaminated noise by using wavelet transform (WT) approach which implemented discrete wavelet transform (DWT) method of the wavelet-denoising signal. Subsequently,the filtered signal containing useful information was extracted by three methods  root mean square (RMS),mean absolute value (MAV),and autoregressive (AR) covariance,all of which are commonly used in TD.A comparative analysis of the three different techniques was performed based on the accuracy performance of the EMG pattern classification using linear vector quantization (LVQ) neural network.In the experimental work undertaken,six healthy subjects comprised of males and females were selected. Three sets of resistance band loads,namely 5 kg,9 kg,and 16 kg were used as a force during the biceps brachii muscle contraction in the rehabilitation exercise.Each of the subject was required to perform three levels of the arm angle positions (30˚, 90˚,and 150˚) for each set of resistance band load.The results of the experiment showed that Daubechies6 (db6) was the most appropriate DWT method including a 6-level decomposition,upholding soft rigrsure and heursure threshold rules,and a single-level threshold rescaling for the wavelet denoising signal analysis.From the three different techniques in extract feature vector as an input for LVQ classifier,the study concluded that the best system performance was the AR covariance method, where it obtained the average percentage of 95.56% for all classes in the EMG pattern recognition.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Burhan, Nuradebah
author_facet	Burhan, Nuradebah
author_sort	Burhan, Nuradebah
title	Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition
title_short	Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition
title_full	Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition
title_fullStr	Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition
title_full_unstemmed	Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition
title_sort	spectral estimation and supervised classification technique for real time electromyography pattern recognition
granting_institution	UTeM
granting_department	Faculty Of Electrical Engineering
publishDate	2018
url	http://eprints.utem.edu.my/id/eprint/23299/1/Spectral%20Estimation%20And%20Supervised%20Classification%20Technique%20For%20Real%20Time%20Electromyography%20Pattern%20Recognition.pdf http://eprints.utem.edu.my/id/eprint/23299/2/Spectral%20Estimation%20And%20Supervised%20Classification%20Technique%20For%20Real%20Time%20Electromyography%20Pattern%20Recognition.pdf
_version_	1747834030775074816
spelling	my-utem-ep.232992022-03-17T12:14:06Z Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition 2018 Burhan, Nuradebah T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Electromyography (EMG) signal is a biomedical signal which measures physical activity of human muscle.It has been acknowledged to be widely used in rehabilitation or recovery application system assisting physiotherapist to monitor a patient’s physical strength,function,motion and overall well-being by addressing the underlying physical issues.In application system associated with rehabilitation,a signal processing and classification techniques are implemented to classify EMG signal obtained.For real time application in the rehabilitation, the classification is crucial issue.The success of the signal classification depends on the selection of the features that represent a raw EMG signal in the signal processing.Therefore,a robust and resilient denoising method and spectral estimation technique have been acknowledged as necessary to distinguish and detect the EMG pattern.The present study was undertaken to determine the characteristic of EMG features using denoising method and spectral estimation technique for assessing the EMG pattern based on a supervised classification algorithm.In the study,the combination of time-frequency domain (TFD) and time domain (TD) were identified as the preferred denoising method and spectral estimation techniques.In the first part of study, the recorded EMG signal filtered the contaminated noise by using wavelet transform (WT) approach which implemented discrete wavelet transform (DWT) method of the wavelet-denoising signal. Subsequently,the filtered signal containing useful information was extracted by three methods  root mean square (RMS),mean absolute value (MAV),and autoregressive (AR) covariance,all of which are commonly used in TD.A comparative analysis of the three different techniques was performed based on the accuracy performance of the EMG pattern classification using linear vector quantization (LVQ) neural network.In the experimental work undertaken,six healthy subjects comprised of males and females were selected. Three sets of resistance band loads,namely 5 kg,9 kg,and 16 kg were used as a force during the biceps brachii muscle contraction in the rehabilitation exercise.Each of the subject was required to perform three levels of the arm angle positions (30˚, 90˚,and 150˚) for each set of resistance band load.The results of the experiment showed that Daubechies6 (db6) was the most appropriate DWT method including a 6-level decomposition,upholding soft rigrsure and heursure threshold rules,and a single-level threshold rescaling for the wavelet denoising signal analysis.From the three different techniques in extract feature vector as an input for LVQ classifier,the study concluded that the best system performance was the AR covariance method, where it obtained the average percentage of 95.56% for all classes in the EMG pattern recognition. 2018 Thesis http://eprints.utem.edu.my/id/eprint/23299/ http://eprints.utem.edu.my/id/eprint/23299/1/Spectral%20Estimation%20And%20Supervised%20Classification%20Technique%20For%20Real%20Time%20Electromyography%20Pattern%20Recognition.pdf text en public http://eprints.utem.edu.my/id/eprint/23299/2/Spectral%20Estimation%20And%20Supervised%20Classification%20Technique%20For%20Real%20Time%20Electromyography%20Pattern%20Recognition.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=112688 mphil masters UTeM Faculty Of Electrical Engineering 1. Akdemir Akar, S., Kara, S. and Bilgiç, V., 2016. Investigation of heart rate variability in major depression patients using wavelet packet transform. Psychiatry Research, 238, pp. 326–332. 2. Al-Mulla, M.R., Sepulveda, F. and Colley, M., 2011. A review of non-invasive techniques to detect and predict localised muscle fatigue. Sensors, 11(4), pp. 3545–3594. 3. Al-qazzaz, N.K., Mohd Ali., S.H., Ahmad, S.A., Islam, M.S., and Escudero, J., 2015. Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task. Sensors, 15(11), pp. 29015–29035. 4. Altın, C. and Er, O., 2016. Comparison of Different Time and Frequency Domain Feature Extraction Methods on Elbow Gesture’s EMG. European Journal of Interdisciplinary Studies, 5(1), pp. 35–44. 5. Ambar.R., Ahmad.M.S., and Abdul Jamil. M.M., 2011. Design and Development of Arm Rehabilitation Monitoring Device. 5th Kuala Lumpur International Conference on Biomedical Engineering, pp. 781-784. 6. Baba, T., 2012. Time-frequency analysis using short time fourier transform. The Open Acoustics Journal, 5(1), pp. 32–38.106 7. Bai, F., Lubecki T., Chew, C.M., and Teo, C.L., 2012. Novel time-frequency approach for muscle fatigue detection based on sEMG. 2012 IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Electronics and Systems for Better Life and Better Environment, BioCAS 2012 - Conference Publications, pp. 364–367. 8. Balakrishnan, P.V., Cooper, M.C., Jacob, V.S., and Lewis, P.A., 1994. A Study of the Classification Capabilities of Neural Network using Unsupervised Learning: A Comparison with K-Means Clustering. The Psychometric Society, 59(4), pp. 509–525. 9. Baranwal, N., Singh, N. and Nandi, G.C., 2014. Indian Sign Language gesture recognition using Discrete Wavelet Packet Transform. 2014 International Conference on Signal Propagation and Computer Technology, pp. 573–577. 10. Beck, T.W., Defreitas, J.M. and Stock, M.S., 2010. An examination of cross-talk among surface mechanomyographic signals from the superficial quadriceps femoris muscles during isometric muscle actions. Human Movement Science, 29(2), pp. 165–171. 11. Belavagi, M.C. and Muniyal, B., 2016. Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection. In Procedia Computer Science. The Author(s), pp. 117–123. 12. Berlia, R., Kandoi, S., Dubey, S., and Pengali, T.R., 2014. Gesture Based Universal Controller Using EMG Signals. Proceedings of International Conference on Circuits, Communication, Control and Computing (I4C 2014), pp. 165–168.107 13. Burke, D., 2010. Whither needle EMG? Clinical Neurophysiology, 121(9), pp. 1373–1375. 14. Camata, T. V., Dantas, J., Abrao, T., Brunetto, M.A.O.C.,and Moraes, A.C., 2010. Fourier and Wavelet Spectral Analysis of EMG signals in Supramaximal Constant Load Dynamic Exercise. 32nd Annual International Conference of the IEEE EMBS Buenos Aires, Argentina, pp. 1364–1367. 15. Canal, M.R., 2010. Comparison of wavelet and short time Fourier Transform methods in the analysis of EMG signals. Journal of Medical Systems, 34(1), pp. 91–94. 16. Chen, X. and Wang, Z.J., 2013. Pattern recognition of number gestures based on a wireless surface EMG system. Biomedical Signal Processing and Control, 8(2), pp. 184–192. 17. Chowdhury, R.H., Raez, M., Ali, M., Bakar, A., Chellapan, K., and Chang, T.G., 2013. Surface electromyography signal processing and classification techniques. Sensors, 13(9), pp. 12431–12466. 18. Chujit, G., Phinyomark, A., Hu, H., Phukpattaranont, P., and Limsakul, C., 2012. Evaluation of EMG Feature Extraction for Classification of Exercises in Preventing Falls in the Elderly. International PSU Engineering Conference, pp. 1–6. 19. Cosi, P., Frasconi, P., Gori, M., Lastrucci, L., and Soda, G., 2000. Competitive radial basis functions training for phone classification. Neurocomputing, 34(1–4), pp. 117–129. 20. Dai, L. and Duan, F., 2015. Comparison of sEMG-based Feature Extraction and Hand Motion Classification Methods. 2015 11th International Conference on Natural Computation (ICNC), pp. 881–882.108 21. De Luca, C.J., Donald, L., Gilmore, M., Kuznetsov, M., Roy, S.H., 2010. Filtering the surface EMG signal: Movement artifact and baseline noise contamination. Journal of Biomechanics, 43(8), pp. 1573–1579. 22. Djermanova, N., Marinov, M., Ganev, B., Tabakov, S., and Nikoov, G., 2016. LabVIEW Based ECG Signal Acquisition and Analysis. Proc. XXV International Scientific Conference Electronics, pp. 1–4. 23. Elamvazuthi, I., Ling, G., Nurhanim, K., Vasant, P., and Parasuranam, S., 2013. Surface electromyography (sEMG) feature extraction based on Daubechies wavelets. Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013, pp. 1492–1495. 24. Englehart, K., Hudgins, B., Parker, P., and Stevenson, M., 1999. Classification of the myoelectric signal using time-frequency based representations. Medical Engineering and Physics, 21, pp. 431–438. 25. Ergen, B., 2013. Comparison of Wavelet Types and Thresholding Methods on Wavelet Based Denoising of Heart Sounds. Journal of Signal and Information Processing, 4, pp. 164–167. 26. Farfán, F.D., Politti, J.C. and Felice, C.J., 2010. Evaluation of EMG processing techniques using Information Theory. Biomedical Engineering Online, 9(72), pp. 1–18. 27. Fattah, S.A., Ud, A., Shahnaz, C., Zhu, W., and Ahmad, O., 2013. Identification of Motor Neuron Disease Using Wavelet Domain Features Extracted from EMG Signal. 2013 IEEE109 International Symposium on Circuits and Systems (ISCAS), pp. 1308–1311. 28. Fu, Z. et al., 2015. EMG recording of wrist gestures under non-ideal electrode placement for machine control in a manufacturing environment. Jurnal Teknologi, 77(21), pp. 41–49. 29. Ganança, M.M., Caovilla, H.H. & Ganança, F.F., 2010. Electronystagmography versus videonystagmography. Brazilian Journal of Otorhinolaryngology, 76(3), pp. 399–403. 30. Gao, Y. and Lv, Q., 2010. A method to judge the pupil position and measure the eye rotational angle. International Conference of Medical Image Analysis and Clinical Application (MIACA), pp. 9–11. 31. Garcia, G.A., Zaccone, F., Ruff, R., Micera, S., Hoffmann, K., and Dario,O., 2007. Characterization of a New Type of Dry Electrodes for Long-Term Recordings of SurfaceElectromyogram. In 2007 IEEE 10th International Conference on Rehabilitation Robotics. IEEE, pp. 849–853. 32. Gradolewski, D., Tojza, M., P., Jaworski, J., Ambroziak, D., Redlarski, G., and Krawozuk, M., 2015. Arm EMG Wavelet-Based Denoising System. Mechatronics Ideas for Industrial Application, 317, pp. 289-296. 33. Gualsaqui Miranda, M. V., Vizcaino Espinosa, I.P. and Flores Calero, M.J., 2016. ECG signal features extraction. Ecuador Technical Chapters Meeting (ETCM), IEEE, pp. 1–6. 34. Guoyuon, Z., Junchao, W., Yao, C., and Xiutian, Y., 2011. Wavelet Transfrom TimeFrequency Analysis Method for the Time-Varying Non-Stationary Signal. Advances in110 Electrical Engineering and Electrical Machines, 134, pp. 43-50. 35. Hamedi, M., Salleh, S., Noor, A.M.,, Swee, T.T.,, and Afizam, I.K., 2012. Comparison of Different Time-domain Feature Extraction Methods on Facial Gestures’ EMGs. Progress In Elctromagnetics Research Symposium Proceedings. pp. 1897–1900. 36. Hang, W., Choi, K., Wang, S., and Qian, P., 2017. Semi-supervised learning using hidden feature augmentation. Applied Soft Computing Journal, 59, pp. 448–461. 37. Hardalaç, F., Yildirim, H. and Serhatlioǧlu, S., 2007. Determination of carotid disease with the application of STFT and CWT methods. Computers in Biology and Medicine, 37(6), pp. 785–792. 38. Hefftner, G., Zucchini, W. and Jaros, G.G., 1988. The Electromyogram ( EMG ) as a Control Signal for Functional Neuromuscular Stimulation-Part I : Autoregressive Modeling as a Means of EMG Signature Discrimination. IEEE Transactions on Biomedical Engineering, 35(4), pp. 230–237. 39. Hudgins, B., Parker, P. and Scott, R.N., 1993. A New Strategy for Multifunction Myoelectric Control. IEEE Transactions on Biomedical Engineering, 40(1), pp. 82–94. 40. Huybrechs, D. and Vandewalle, S., 2006. A two-dimensional wavelet-packet transform for matrix compression of integral equations with highly oscillatory kernel. Journal of Computational and Applied Mathematics, 197, pp. 218–232. 41. Jacobson, W.C., Gabel, R.H. and Brand, R.A., 1995a. Insertion of fine-wire electrodes does not alter EMG patterns in normal adults. Gait and Posture, 3(1), pp. 59–63.111 42. Jacobson, W.C., Gabel, R.H. and Brand, R.A., 1995b. Surface vs. fine-wire electrode ensemble-averaged signals during gait. Journal of Electromyography and Kinesiology, 5(1), pp. 37-44. 43. Jaffe, D.M., Solomon, N., Robinson, R., Hoffman, H.T., and Luschei, E.S., 1998. Comparison of concentric needle versus hooked-wire electrodes in the canine larynx. Otolaryngology - Head and Neck Surgery, 118(5), pp. 655–662. 44. Jali, M.H., Ibrahim, I.M., Sulaima, M., Bukhari, W.M., Izzudin, T.A., and Nasir, M.N., 2015. Features extraction of EMG signal using time domain analysis for arm rehabilitation device. International Conference on Mathematics, Engineering and Industrial Applications, 1660, pp. 1–9. 45. Jamal, M.Z., 2012. Signal Acquisition Using Surface EMG and Circuit Design Considerations for Robotic Prosthesis. Computational Intelligence in Electromyography Analysis - A Perspective on Current Applications and Future Challenges, pp. 427–448. 46. Jayaseelan, D.J. and Magrum, E.M., 2012. Eccentric training for the rehabilitation of a high level wrestler with distal biceps tendinosis: a case report. International journal of sports physical therapy, 7(4), pp. 413–424. 47. Kakoty, N.M. and Hazarika, S.M., 2011. Recognition of grasp types through principal components of DWT based EMG features. In IEEE International Conference on Rehabilitation Robotics. pp. 1–6. 48. Karlık, B., 2014. Machine Learning Algorithms for Characterization of EMG Signals.112 International Journal of Information and Electronics Engineering, 4(3), pp. 189–194. 49. Karlsson, S., Yu, J. and Akay, M., 2000. Time-Frequency Analysis of Myoelectric Signals During Dynamic Contractions: A Comparative Study. IEEE Transactions on Biomedical Engineering, 47(2), pp. 228–238. 50. Karthikeyan, P., Murugappan, M. and Yaacob, S., 2012. EMG Signal Based Human Stress Level Classification Using Wavelet Packet Transform. Springer-Verlag Berlin Heidelberg, pp. 236–243. 51. Khalid, E.B., Salman, A., Khan, Z.Y., Faris, M., Mujib, and M.D., Ansari, T.A., 2017. Design and Development of Muscle Monitoring system to visualize and monitor the activity of Biceps Brachii with varying load. International Conference on Innovations in Electrical Engineering and Computational Technologies (ICIEECT), pp. 1-4. 52. Khezri, M. and Jahed, M., 2007. Real-time intelligent pattern recognition algorithm for surface EMG signals. Biomedical engineering online, 6, pp. 45. 53. Khorrami, H. and Moavenian, M., 2010. A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert Systems with Applications, 37(8), pp. 5751–5757. 54. Kiatpanichagij, K. and Afzulpurkar, N., 2009. Use of supervised discretization with PCA in wavelet packet transformation-based surface electromyogram classification. Biomedical Signal Processing and Control, 4, pp. 127–138.113 55. Kiymik, M.K., Guler, I., Dizibuyuk, A., and Akin, M., 2005. Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for realtime application. Computers in Biology and Medicine, 35(7), pp. 603–616. 56. Kostsiantis, S.B., 2007. Supervised Machine Learning: A Review of Classification Technique. In Emerging Artificial Intelligence Applications in Computer Engineering. pp. 3–24. 57. Kumar, J.S. and Bhuvaneswari, P., 2012. Analysis of electroencephalography (EEG) signals and its categorization - A study. International Conference on Modeling, Optimization and Computing (ICMOC 2012), 38, pp. 2525–2536. 58. Lee, J.-Y., 2013. Variable short-time Fourier transform for vibration signals with transients. Journal of Vibration and Control, 21(7), pp. 1–15. 59. Li, G., Wang, S. and Duan, Y.Y., 2017. Towards gel-free electrodes: A systematic study of electrode-skin impedance. Sensors and Actuators, B: Chemical, 241, pp. 1244–1255. 60. Liu, J., 2014. Feature dimensionality reduction for myoelectric pattern recognition : A comparison study of feature selection and feature projection methods. Medical Engineering and Physics, 36(12), pp. 1716–1720. 61. Liu, Z. and Luo, Z., 2008. Hand Motion Pattern Classifier Based on EMG Using Wavelet Packet Transform and LVQ Neural Networks. In Proceedings of 2008 IEEE International Symposium on IT in Medicine and Education. pp. 28–32. 62. Lubis, M.Z. and Manik, H.M., 2016. Signal Processing for Power Spectral Density (PSD)114 2016th ed., Signal processing for marine acoustic and dolphin using matlab, Edition: 2016, Chapter: 2, LAP LAMBERT Academic Publishing. 63. Mahdavi, F.A., Ahmad, S., Marhaban, M., Akhbarzadeh-T, M., 2012. Surface Electromyography Feature Extraction Based on Wavelet Transform. International Journal of Integrated Engineering, 4(3), pp. 1–7. 64. Manzanera, O.M., Eting, J.W., Hoeven, J.H.V.D., and Maurits, N.M., 2016. Tremor Detection Using Parametric and Non- Parametric Spectral Estimation Methods : A Comparison with Clinical Assessment. PLOS ONE, 11(6), pp. 1–15. 65. Mccool, P., Petropoulakis, L., Soraghan, J.J., Chatlani, N., 2015. Improved pattern recognition classification accuracy for surface myoelectric signals using spectral enhancement. Biomedical Signal Processing and Control, 18, pp. 61–68. 66. Millett, D., Coutin-Churchman, P. and Stern, J.M., 2015. Basic Principles of Electroencephalography, Brain Mapping: An Encyclopedic Reference, pp. 75-80. 67. Mortazavi, S.H. and Shahrtash, S.M., 2008. Comparing Denoising Performance of DWT , WPT , SWT and DT-CWT for Partial Discharge Signals. Universities Power Engineering Conference, pp. 1–6. 68. Muhaseena, T.K. and Lekshmi, M.S., 2016. A Model for Pitch Estimation Using Wavelet Packet Transform Based Cepstrum Method. Procedia Technology, 24, pp. 1061–1067. 69. N.Guerrero, F. and Spinelli, E., 2014. Surface EMG Multichannel Measurements Using115 Active, Dry Branched Electrodes. VI Latin American Congress on Biomedical Engineering CLAIB 2014, 49, pp. 1–4. 70. Nayak, M. and Sankar Panigrahi, B., 2011. Advanced Signal Processing Techniques for Feature Extraction in Data Mining. International Journal of Computer Applications, 19(9), pp. 30–37. 71. Nazmi, N., Abdul Rahman, M., Yamamoto, S., Ahmad, S.A., Hairi, Z., and Mazlan, S., 2016. A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions. Sensors, 16(8), pp. 1–28. 72. Negi, S., Kumar, Y. and Mishra, V., 2016. Feature Extraction and Classification for EMG Signals Using Linear Discriminant Analysis. Advances in Computing, Communication, & Automation (ICACCA) (Fall), International Conference on, pp. 1–6. 73. Nikam, S.S., 2015. A Comparative Study of Classification Techniques in Data Mining Algorithms. Oriental Journal of Computer Science & Technology, 8(1), pp. 13–19. 74. Norali, A. and Som, M., 2009. Surface electromyography signal processing and application : A review. Proceedings of the International Conference on Man-Machine Systems (ICoMMS), pp. 1–9. 75. Nova, D. and Estevez, P.A., 2013. A review of learning vector quantization classifiers. Neural Computation & Application, 25(3–4), pp. 511–524.116 76. Oladazimi, M., Vaneghi, F., Safari, M., Asadi, H., and Kaboli, S.H., 2015. A Review for Feature Extraction of EMG Signal Processing. International Conference on Computer and Automation Engineering, 4th (ICCAE 2012), pp. 85–93. 77. Omari, T. and Bereksi-Reguig, F., 2015. An automatic wavelet denoising scheme for heart sounds. International Journal of Wavelets, Multiresolution and Information Processing, 13(3), pp.1–22. 78. Oskoei, M.A. and Hu, H., 2006. GA-based Feature Subset Selection for Myoelectric Classification. International Conference on Robotics and Biomimetics, pp. 1465–1470. 79. Oskoei, M.A. and Hu, H., 2007. Myoelectric control systems — A survey. Biomedical Signal Processing and Control, 2(4), pp. 275–294. 80. Peng, H., Wang, J., Perez-Jimenez, M., and Risco-Nunez, A., 2015. An unsupervised learning algorithm for membrane computing. Information Science, 304, pp. 80–91. 81. Phinyomark, A., Limsakul, C., and Phukpattaranont, P., 2009. EMG Denoising Estimation Based on Adaptive Wavelet Thresholding for Multifunction Myoelectric Control. Innovative Technologies in Intelligent Systems and Industrial Applications, pp. 171-176. 82. Phinyomark, A., Phukpattaranont, C., and Limsakul, C., 2012a. Feature extraction and reduction of wavelet transform coefficients for EMG pattern classification. Elektronika ir Elektrotechnika, 122(6), pp. 27–32.117 83. Phinyomark, A., Phukpattaranont, P. and Limsakul, C., 2012b. Feature reduction and selection for EMG signal classification. Expert Systems with Applications, 39(8), pp. 7420– 7431. 84. Phinyomark, A., Quaine, F., Charbonnier, S., Serviere, C., Tarpin-Bernard, F., and Laurillau, Y., 2013. EMG feature evaluation for improving myoelectric pattern recognition robustness. Expert Systems with Applications, 40(12), pp. 4832–4840. 85. Phinyomark, A., Quaine, F., Charbonnier, S., Serviere, C., Tarpin-Bernard, F., and Laurillau, Y., 2014. Feature extraction of the first difference of EMG time series for EMG pattern recognition. Computer Methods and Programs in Biomedicine, 117(2), pp. 247–256. 86. Podrug, E. and Subasi, A., 2015. Surface EMG pattern recognition by using DWT feature extraction and SVM classifier. 1st Conference of Medical and Biological Engineering in Bosnia and Herzegovina. pp. 1–3. 87. Poo, T.S., and Sundaraj, K., 2010. Design and Development of Low Cost Biceps Tendonitis Monitoring System using EMG Sensor. 6th International Colloqium on Signal Processing & Its Application (CSPA), pp. 288-292. 88. Raez, M.B.I., Hussain, M.S. and Mohd-Yasin, F., 2006. Techniques of EMG signal analysis: detection, processing, classification and applications. Biological procedures online, 8(1), pp. 11–35. 89. Riillo, F., Quitadamo, F., Cavrini, F., Gruppioni, E., Pinto, C.A., Pasto, N.C., Sbernini, L., Albero, L., and Saggio, G., 2014. Optimization of EMG-based hand gesture recognition:118 Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees. Biomedical Signal Processing and Control, 14(1), pp. 117–125. 90. Rubin, D.I., 2012. Needle electromyography: Basic concepts and patterns of abnormalities. Neurologic Clinics, 30(2), pp. 429–456. 91. Sahin, U. and Sahin, F., 2012. Pattern Recognition with surface EMG Signal based Wavelet Transformation. 2012 IEEE International Conference on Systems, Man, and Cybernetics, pp. 295–300. 92. Sandberg, A., Hansson, B. and Stålberg, E., 1999. Comparison between concentric needle EMG and macro EMG in patients with a history of polio. Clinical Neurophysiology, 110(11), pp. 1900–1908. 93. Shemi, P. and Shareena, E., 2016. Analysis of ECG Signal Denoising Using Wavelet Transform. 2nd IEEE International Conference on Engineering and Technology (ICETECH), pp. 1–6. 94. Stegeman, D.F., Gootzen, T.H., Theeuwen, M.M., Vingerhoets, H.J., 1994. Intramuscular potential changes caused by the presence of the recording EMG needle electrode. Electroencephalography and Clinical Neurophysiology/ Evoked Potentials, 93(2), pp. 81– 90. 95. Su, Y.C., Lian, K.L. and Chang, H.H., 2011. Feature selection of non-intrusive load monitoring system using STFT and wavelet transform. Proceedings - 2011 8th IEEE International Conference on e-Business Engineering, pp. 293–298.119 96. Subasi, A., 2012, Classification of EMG signals using combined features and Soft Computing Techniques. Applied Soft Computing, 12, pp. 2188-2198. 97. Subasi, A. and Kiymik, M.K., 2010. Muscle fatigue detection in EMG using time-frequency methods, ICA and neural networks. Journal of Medical Systems, 34(4), pp. 777–785. 98. Subasi, A., Yilmaz, M. and Ozcalik, H.R., 2006. Classification of EMG signals using wavelet neural network. Journal of Neuroscience Methods, 156(1–2), pp. 360–367. 99. Sueaseenak, D., Chanwimalueang, C., and Pintavirooj, C., 2013. An accurate forearm EMG signal classification method using two-channel electrode. IEEJ Transactions on Electrical and Electronic Engineering, 8(4), pp. 328–338. 100. Suen, Y., Xiao, S., Xiong, Y., Hao, S., and Zhao, X., 2015. Time-Frequency Analysis System Based on Temporal Fourier Transform. IEEE International Conference on Communication Problem-Solving, pp. 97–100. 101. Tsai, A.C., Hsieh, T.H., Luh, J.J., and Lin, T.T., 2014. A comparison of upper-limb motion pattern recognition using EMG signals during dynamic and isometric muscle contractions. Biomedical Signal Processing and Control, 11(1), pp. 17–26. 102. Uddin, N., Sundaraj, K., Ahmad, R., Nadarajah, S., Shi, P., 2011. Recent Survey of Automated Rehabilitation Systems Using EMG Biosensors. Journal of Physical Therapy Science, 23(6), pp. 945-948. 103. Uddin, N., Sundaraj, K., Alqahtani, M., Altwirij, O., 2014. EMG-force relationship during static contraction : Effects on sensor placement locations on biceps brachii muscle.120 Technology and Health Care, 22, pp. 505–513. 104. Valencia, D., Orejuela, D., Salazar, J., and Valencia, J., 2016. Comparison analysis between rigrsure, sqtwolog, heursure and minimaxi techniques using hard and soft thresholding methods. 2016 21st Symposium on Signal Processing, Images and Artificial Vision, pp. 1–5. 105. Wan, B., Xu, L., Ren, Y., Wang, L., Qiu, S., Liu, X., Qi, H., and Ming, D., 2010. Study on Fatigue Feature from Forearm SEMG Signal Based on Wavelet Analysis. Proceedings of the 2010 IEEE International Conference on Robotics and Biomimetics, pp. 1229–1232. 106. Wang, C.C. and Kang, Y., 2012. Feature Extraction Techniques of Non-Stationary Signals for Fault Diagnosis in Machinery Systems. Journal of Signal and Information Processing, 3, pp. 16–25. 107. Wang, D., Zhang, X., Cheng, X., and Zhou, P., 2014. Application of Wavelet Packet Transform on Myoelectric Pattern Recognition for Upper Limb Rehabilitation after Stroke. In 36th Annual International Conference of the IEEE Engineering in Medicince and Biology Society. pp. 3578–3581. 108. Wheeler, K., 2003. Device Control Using Gestures Senses From EMG. Proceedings of the 2003 IEEE International Conference on Soft Computing in Industrial Applications, pp.21– 26. 109. Wijaya, D.R., Sarno, R. and Zulaika, E., 2016. Information Quality Ratio as a novel metric for mother wavelet selection. Chemometrics and Intelligent Laboratory Systems, 160, pp.121 59-71. 110. Xing, K., Yang, P., Huang, J., Wang, J., and Zhu, Q., 2014. A real-time EMG pattern recognition method for virtual myoelectric hand control. Neurocomputing, 136, pp. 345– 355. 111. Yahya, A.B., Daud, W., Horng, C.S., and Sudirman, R., 2014. Electromyography Signal on Biceps Muscle in Time Domain Analysis. Journal of Mechanical Engineering and Sciences, 7, pp. 1179–1188. 112. Yu, S., You, X., Ou, W., Jiang, X., Zhao, K., Xhu, Z., Mou, Y., and Zhao, X., 2016. STFTlike time frequency representations of nonstationary signal with arbitrary sampling schemes. Neurocomputing, 204, pp. 211–221. 113. Zahak, M., 2012. Signal Acquisition Using Surface EMG and Circuit Design Considerations for Robotic Prosthesis. Computational Intelligence in Electromyography Analysis - A Perspective on Current Applications and Future Challenges, pp. 427–448. 114. Zhang, Y., 2017. Extracting time-frequency feature of single- channel vastus medialis EMG signals for knee exercise pattern recognition. PLOS ONE, 12(7), pp. 1–18. 115. Zheng Bong, S., Wan, K., Murugappan, M., Mohamed Ibrahim, N., Rajamanickam, Y., and Mohammad, K., 2017. Biomedical Signal Processing and Control Implementation of wavelet packet transform and non linear analysis for emotion classification in stroke patient using brain signals. Biomedical Signal Processing and Control, 36, pp. 102–112

Spectral Estimation And Supervised Classification Technique For Real Time Electromyography Pattern Recognition

Similar Items