Wavelet Based Machine Learning Technique to Classify the Different Shoulder Movement of Upper Limb Amputee

[1] K. Ziegler-Graham, E. J. MacKenzie, P. L. Ephraim, T. G. Travison, and R. Brookmeyer, Estimating the prevalence of limb loss in the united states: 2005 to 2050, Archives of Physical Medicine and Rehabilitation, 89(2008) 422–429.

DOI: 10.1016/j.apmr.2007.11.005

Google Scholar

[2] Kaur A, Agarwal R, Kumar A. Comparison of muscles activity of abled bodied and amputee subjects for around shoulder movement. Biomed Mater Eng. 27 (2016) 29-37.

DOI: 10.3233/bme-161565

Google Scholar

[3] R.T. Floyd. The Shoulder Girdle, Manual of Structural Kinesiology, Chapter 4. The McGraw-Hill Companies, (2012).

Google Scholar

[4] Soma Hirokazu, Horiuchi Yuse, Gonzalez Jose and Wenwei Yu. Classification of Upper Limb Motions from Around-Shoulder Muscle Activities, Proceeding of IEEE Int. Conf. on Rehabilitation Robotics Rehab Week Zurich. ETH Zurich Science City, Switzerland (2011).

DOI: 10.1109/icorr.2009.5209477

Google Scholar

[5] Min-Hyeok Kang, Jae-Seop, Jun-Hyeok. Jang Differences in Muscle Activities of the Infraspinatus and Posterior Deltoid during Shoulder External Rotation in Open Kinetic Chain and Closed Kinetic Chain Exercises. J Phys Ther Sci. 26 (2014) 895–897.

DOI: 10.1589/jpts.26.895

Google Scholar

[6] Robert A. McCabe, Karl F. Orishimo, Malachy P. McHugh, Stephen J. Nicholas. Surface Electromygraphic Analysis of the Lower Trapezius Muscle During Exercises Performed Below Ninety Degrees of Shoulder Elevation in Healthy Subjects. N Am J Sports Phys Ther 2 (2007).

Google Scholar

[7] A. Phinyomark1, C. Limsakul, P. Phukpattaranont. Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification, Measurement Science Review 11 (2011) 45-52.

DOI: 10.2478/v10048-011-0009-y

Google Scholar

[8] Phinyomark, A., Hirunviriya, S., Limsakul, C., Phukpattaranont, P. Evaluation of EMG feature extraction for hand movement recognition based on euclidean distance and standard deviation. 7th International Conference on Electrical Engineering/Electronics, Computer, Telecommunica-tions and Information Technology, Chiang Mai, Thailand (2010).

DOI: 10.1007/978-3-642-21729-6_183

Google Scholar

[9] Wang, G., Wang, Z., Chen, W., Zhuang, J. Classification of surface EMG signals using optimal wavelet packet method based on Davies–Bouldin criterion. Med. Biol. Eng. Comput 44 (2006). 865-872.

DOI: 10.1007/s11517-006-0100-y

Google Scholar

[10] A. Phinyomark, A. Nuidod, P. Phukpattaranont, C. Limsakul Feature Extraction and Reduction of Wavelet Transform Coefficients for EMG Pattern Classification, Electronics and electrical engg 2 (2012) 27-33.

DOI: 10.5755/j01.eee.122.6.1816

Google Scholar

[11] A. B. M. S. U. Doulah, S. A. Fattah, W. -P. Zhu. Wavelet Domain Feature Extraction Scheme Based on Dominant Motor Unit Action Potential of EMG Signal for Neuromuscular Disease Classification, IEEE Transactions On Biomedical Circuits And Systems 8 (2014).

DOI: 10.1109/tbcas.2014.2309252

Google Scholar

[12] Kaur A, Agarwal R, Kumar A. A Combined Statistical and Time-Frequency Approach to the Analysis of Electromyography Signals. Advances in Metrology (2014) 19-21.

Google Scholar

[13] Englehart, K., Hudgins, B., Parker, P.A. A wavelet-based continuous classification scheme for multifunction myoelectric control, IEEE Trans. Biomed. Eng. 48 (2001) 302-311.

DOI: 10.1109/10.914793

Google Scholar

[14] Gokgoz E, Subasi A. Comparison of decision tree algorithms for EMG signal classification using DWT. Biomedical Signal Processing and Control 18 2015)138-144.

DOI: 10.1016/j.bspc.2014.12.005

Google Scholar

[15] Shuxiang G, Muye Pang, Baofeng Gao, Hideyuki H, Hidenori, I. Comparison of sEMG-Based Feature Extraction and Motion Classification Methods for Upper-Limb Movement. Sensors 15 (2015) 9022-9031.

DOI: 10.3390/s150409022

Google Scholar

[16] Fadlalla A. An experimental investigation of the impact of aggregation on the performance of data mining with logistic regression. Journal of Information & Management 42 (2005)695-707.

DOI: 10.1016/j.im.2004.04.005

Google Scholar

[17] Marijana ZS, Sanja P, N Sarlija. A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem. Business Systems Research 5 (2014) 82-96.

Google Scholar

[18] Raez M.B.I., Hussain M.S., Mohd-Yasin, F. Techniques of EMG signal analysis: detection, processing, classification and applications. Bio Proced Online 8 (2006) 12-35.

DOI: 10.1251/bpo115

Google Scholar

[19] Farzaneh Akhavan Mahdavi, Siti Anom Ahmad, Mohd Hamiruce Marhaban, and Mohammad-R. Akbarzadeh, The Utility of Wavelet Transform in Surface Electromyography Feature Extraction A Comparative Study of Different MotherWavelets, Measurement Science Review 12 (2012).

Google Scholar

[20] M. S. Hussain1, Md. Mamun, Effectiveness of the Wavelet Transform on the Surface EMG to Understand the Muscle Fatigue during Walk, School of Electrical and Information Engineering, University of Sydney, NSW Australia (2006) 28-33.

DOI: 10.2478/v10048-012-0005-x

Google Scholar

[21] Walker, J. A Primer on Wavelets and Their Scientific Applications. Chapman & Hall. (1999).

Google Scholar

[22] González Jose, Horiuchi Yuse, and Wenwei Yu, Classification of Upper Limb Motions from Around-Shoulder Muscle Activities: Hand Biofeedback, The Open Medical Informatics Journal 4 (2010) 74-80.

DOI: 10.2174/1874431101004020074

Google Scholar

[23] Horiuchi Yuse, González Jose. A study on classification of upper limb motions from around- shoulder muscle activities. Proceeding of ICORR, IEEE International Conference on Rehabilitation Robotics (2009) 311-16.

DOI: 10.1109/icorr.2009.5209477

Google Scholar

[24] Merletti, R, Standards for Reporting EMG Data. Journal of Electromyography and Kinesiology, 1999 III-IV.

Google Scholar

[25] Butterworth, S, On the Theory of Filter Amplifiers. Wireless Engineer (also called Experimental Wireless and the Wireless Engineer 7 (1930) 536–541.

Google Scholar

[26] Phinyomark, A., C. Limsakul, and P. Phukpattaranont, Optimal wavelet functions in wavelet denoising for multifunction myoelectric control. ECTI Transactions on Electrical Eng., Electronics, and Communications. -ECTI 8 (2010) 43-52.

DOI: 10.37936/ecti-eec.201081.172001

Google Scholar

[27] 1M.S. Hussain and 2Md. Mamun, Wavelet Denoising and Surface Electromyography Analysis, School of Electrical and Information Engineering, University of Sydney (2006) 2372-2374.

Google Scholar

[28] Mitchell, T.M. Machine Learning, McGraw-Hill, (1997).

Google Scholar

[29] Qi,Y. Random forest for bioinformatics, Ensemble Machine Learning Methods and Applications 2012. pp.307-323.

DOI: 10.1007/978-1-4419-9326-7_11

Google Scholar

[30] Breiman L. Random forests. Mach. Learn. 2001, pp.5-32.

Google Scholar

[31] Smola A, Vishwanathan SVN. Introduction to Machine Learning; Cambridge university press.

Google Scholar

[32] Ballantyne BT, O'Hare SJ, Paschall JL, et al. Electromyographic activity of selected shoulder muscles in commonly used therapeutic exercises. Phys Ther. 73 (1993) 668–677.

DOI: 10.1093/ptj/73.10.668

Google Scholar