Design and Optimization of Levenberg-Marquardt based Neural Network Classifier for EMG Signals to Identify Hand Motions

[1] Ahsan, M.R., Ibrahimy, M.I., Khalifa, O.O. (2009). EMG signal classification for human computer interaction: A review. European Journal of ScientificResearch, 33 (3), 480-501.Search in Google Scholar

[2] Reaz, M.B.I., Hussain, M.S., Mohd-Yasin, F. (2006). Techniques of EMG signal analysis: Detection, processing, classification and applications. BiologicalProcedures Online, 8 (1), 11-35.Search in Google Scholar

[3] Ahsan, M.R., Ibrahimy, M.I., Khalifa, O.O. (2011). Neural network classifier for hand motion detection from EMG signal. In IFMBE Proceedings, Vol. 35. Springer, 536-541.10.1007/978-3-642-21729-6_135Search in Google Scholar

[4] Ahsan, M.R., Ibrahimy, M.I., Khalifa, O.O. (2010). Advances in electromyogram signal classification to improve the quality of life for the disabled and aged people. Journal of Computer Science, 6 (7), 705-715.10.3844/jcssp.2010.706.715Search in Google Scholar

[5] Hiraiwa, A., Shimohara, K., Tokunaga, Y. (1989). EMG pattern analysis and classification by neural network. In IEEE International Conference onSystems, Man and Cybernetics, 14-17 November 1989. IEEE, 1113-1115.10.1109/ICSMC.1989.71472Search in Google Scholar

[6] Putnam, W., Knapp, R.B. (1993). Real-time computer control using pattern recognition of the electromyogram. In EMBS 1993 : 15th AnnualInternational Conference of the IEEE, 1236-1237.10.1109/IEMBS.1993.979112Search in Google Scholar

[7] Naik, G.R, Kumar, D.K., Singh, V.P., Palaniswami, M. (2006). Hand gestures for HCI using ICA of EMG. In HCSNet Workshop on Use of Vision in Human-Computer Interaction (VisHCI ’06), 1-3 November 2006. Australian Computer Society, 67-72.Search in Google Scholar

[8] Englehart, K., Hudgins, B., Stevenson, M., Parker, P.A. (2002). A dynamic feedforward neural network for subset classification of myoelectric signal patterns. In EMBS 2002 : 17th Annual Conference of the IEEE, 20-25 September 1995. IEEE, Vol. 1, 819-820.Search in Google Scholar

[9] Kelly, M.F., Parker, P.A., Scott, R.N. (2002). The application of neural networks to myoelectric signal analysis: A preliminary study. IEEE Transactions onBiomedical Engineering, 37 (3), 221-230.Search in Google Scholar

[10] Itou, T., Terao, M., Nagata, J., Yoshida, M. (2001). Mouse cursor control system using EMG. In EMBS2001 : 23rd Annual International Conference of theIEEE. Vol. 2, 1368-1369.Search in Google Scholar

[11] Hudgins, B., Parker, P., Scott, R.N. (1993). A new strategy for multifunction myoelectric control. IEEETransactions on Biomedical Engineering, 40 (1), 82-94.10.1109/10.204774Search in Google Scholar

[12] Jung, K.K., Kim, J.W., Lee, H.K., Chung, S.B., Eom, K.H. (2007). EMG pattern classification using spectral estimation and neural network. In Society ofInstrument and Control Engineers (SICE 2007), 17-20 Sept. 2007. IEEE, 1108-1111.Search in Google Scholar

[13] Hagan, M.T., Demuth, H.B., Beale, M.H. (1996). Neural Network Design. Boston, MA: PWS Publishing, 9.1-9.37.Search in Google Scholar

[14] Ahsan, M.R., Ibrahimy, M.I., Khalifa, O.O. (2012). EMG motion pattern classification through design and optimization of neural network. In InternetionalConference on Biomedical Engineering (IcoBE 2012), 27-28 February 2012. IEEE, 175-179.10.1109/ICoBE.2012.6179000Search in Google Scholar

[15] Englehart, K., Hudgins, B. (2003). A robust, real-time control scheme for multifunction myoelectric control. IEEE Transactions on Biomedical Engineering, 50 (7), 848-854.10.1109/TBME.2003.813539Search in Google Scholar

[16] Khezri, M., Jahed, M., Sadati, N. (2007). Neuro-fuzzy surface EMG pattern recognition for multifunctional hand prosthesis control. In IEEE InternationalSymposium on Industrial Electronics, 4-7 June 2007. IEEE, 269-274.10.1109/ISIE.2007.4374610Search in Google Scholar

[17] Tsenov, G., Zeghbib, A.H., Palis, F., Shoylev, N., Mladenov, V. (2007). Neural networks for online classification of hand and finger movements using surface EMG signals. In 8th Seminar on NeuralNetwork Applications in Electrical Engineering(NEUREL 2006), 25-27 September 2006. IEEE, 167-171.Search in Google Scholar

[18] Phinyomark, A., Limsakul, C., Phukpattaranont, P. (2009). A novel feature extraction for robust EMG pattern recognition. Journal of Computing, 1 (1), 71-80.Search in Google Scholar

[19] Lyman, J., Freedy, A., Solomonow, M. (1977). System integration of pattern recognition, adaptive aided, upper limb prostheses. Mechanism and MachineTheory, 12 (5), 503-514.10.1016/0094-114X(77)90045-3Search in Google Scholar

[20] Naik, G.R., Kumar, D.K., Arjunan, S.P. (2010). Pattern classification of myoelectrical signal during different maximum voluntary contractions: A study using BSS techniques. Measurement Science Review, 10 (1), 1-6.Search in Google Scholar

[21] Phinyomark, A., Limsakul, C., Phukpattaranont, P. (2011). Application of wavelet analysis in EMG feature extraction for pattern classification. Measurement Science Review, 11 (2), 45-52.10.2478/v10048-011-0009-ySearch in Google Scholar

[22] Kim, J., Mastnik, S., André, E. (2008). EMG-based hand gesture recognition for realtime biosignal interfacing. In 13th International Conference onIntelligent User Interfaces (IUI ’08), 13-16 January 2008. New York: ACM, 30-39.10.1145/1378773.1378778Search in Google Scholar

[23] Ahsan, M.R., Ibrahimy, M.I., Khalifa, O.O. (2011). Electromyography (EMG) signal based hand gesture recognition using artificial neural network (ANN). In 4th International Conference on Mechatronics (ICOM2011), 17-19 May 2011. IEEE, 1-6.Search in Google Scholar

[24] Khezri, M., Jahed, M. (2007). A novel approach to recognize hand movements via sEMG patterns. In EMBS 2007 : 29th Annual International Conference ofthe IEEE, 23-26 August 2007. IEEE, 4907-4910.10.1109/IEMBS.2007.435344018003106Search in Google Scholar

[25] Cram, J.R., Criswell, E. (2010). Introduction toSurface Electromyography, 2nd ed. Sudbury, MA: Jones and Bartlett Publishers, 320-336.Search in Google Scholar

[26] Hussain, M.S., Mamun, M. (2012). Effectiveness of the wavelet transform on the surface EMG to understand the muscle fatigue during walk. Measurement Science Review, 12 (1), 28-33.10.2478/v10048-012-0005-xSearch in Google Scholar

[27] Subasi, A., Yilmaz, M., Ozcalik, H.R. (2006). Classification of EMG signals using wavelet neural network. Journal of Neuroscience Methods, 156 (1-2), 360-367.10.1016/j.jneumeth.2006.03.00416621003Search in Google Scholar

[28] Zhao, J., Xie, Z., Jiang, L., Cai, H., Liu, H., Hirzinger, G. (2005). Levenberg-Marquardt based neural network control for a five-fingered prosthetic hand. In IEEEInternational Conference on Robotics and Automation, April 2005. IEEE, 4482-4487.10.1109/ROBOT.2005.1570810Search in Google Scholar

[29] Guo, X., Yu, H., Zhen, G., Yuliang, L., Yong, Z., Ying, Z. (2009). Artificial intelligent based human motion pattern recognition and prediction for the surface electromyographic signals. In InternationalConference on Information Technology and ComputerScience (ITCS 2009), 25-26 July 2009. IEEE, 289-292.10.1109/ITCS.2009.65Search in Google Scholar

[30] Naik, G.R., Kumar, D.K., Arjunan, S.P., Weghorn, H., Palaniswami, M. (2007). Limitations and applications of ICA in facial sEMG and hand gesture sEMG for human computer interaction. In 9th BiennialConference of the Australian Pattern RecognitionSociety on Digital Image Computing Techniques andApplications, 3-5 December 2007. IEEE, 15-22.10.1109/DICTA.2007.4426770Search in Google Scholar