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Pattern Recognition Based Prosthesis Control for Movement of Forearms Using Surface and Intramuscular EMG Signals

Authors: Anjana Goen, D. C. Tiwari


Myoelectric control system is the fundamental component of modern prostheses, which uses the myoelectric signals from an individual’s muscles to control the prosthesis movements. The surface electromyogram signal (sEMG) being noninvasive has been used as an input to prostheses controllers for many years. Recent technological advances has led to the development of implantable myoelectric sensors which enable the internal myoelectric signal (MES) to be used as input to these prostheses controllers. The intramuscular measurement can provide focal recordings from deep muscles of the forearm and independent signals relatively free of crosstalk thus allowing for more independent control sites. However, little work has been done to compare the two inputs. In this paper we have compared the classification accuracy of six pattern recognition based myoelectric controllers which use surface myoelectric signals recorded using untargeted (symmetric) surface electrode arrays to the same controllers with multichannel intramuscular myolectric signals from targeted intramuscular electrodes as inputs. There was no significant enhancement in the classification accuracy as a result of using the intramuscular EMG measurement technique when compared to the results acquired using the surface EMG measurement technique. Impressive classification accuracy (99%) could be achieved by optimally selecting only five channels of surface EMG.

Keywords: Discriminant Locality Preserving Projections (DLPP), myoelectric signal (MES), Sparse Principal Component Analysis (SPCA), Time Frequency Representations (TFRs)

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[1] P. Parker, K. Englehart and B. Hudgins, “Myoelectric signal processing for control of powered prosthesis”, J. Electromyogr. Kinesiol., vol. 16, no. 6, pp. 541–548, 2006.
[2] R. N. Khushaba, S. Kodagoda, D. Liu and G. Dissanayake, "Electromyogram based fingers movement recognition using neighborhood preserving analysis with QR-decomposition", Seventh Int. Conf. on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 1-6, 2011.
[3] V. I. Pavlovic, R. Sharma and T. S. Huang, “Visual interpretation of hand gestures for human-computer interaction”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 677-695, 1997.
[4] T. R. Farrell and R. F. ff. Weir, “A Comparison of the Effects of Electrode Implantation and Targeting on Pattern Classification Accuracy for Prosthesis Control”, IEEE Trans Biomed Eng., vol.55, no.9, pp. 2198–2211, 2008.
[5] K. Englehart and B. Hudgins, “A robust, real-time control scheme for multifunction myoelectric control”, IEEE Trans Biomed Eng., vol. 50, pp. 848–854, 2003.
[6] B. Hudgins, P. Parker and R. N. Scott, “A new strategy for multifunction myoelectric control”, IEEE Transaction on Biomedical Engineering, vol. 40, no.1, pp. 82-94, 1993.
[7] K. Englehart, B. Hudgins and A. Philip, “A Wavelet-based continuous classification scheme for Multifunction Myoelectric Control”, IEEE Transactions on Biomedical Engineering, vol. 48, no.3, pp. 302-311, 2001.
[8] A. B. Ajiboye and R. F. ff. Weir, “A Heuristic Fuzzy Logic Approach to EMG Pattern Recognition for Multifunctional Prosthesis Control”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 13, no. 3, pp. 280-291, 2005.
[9] D. Graupe, J. Salahi, and K. H. Kohn, “Multifunction prosthesis and orthosis control via micro-computer identification of temporal pattern differences in single-site myoelectric signals,” J. Biomed. Eng., vol. 4, pp. 17–22, 1982.
[10] R. N. Khushaba, S. Kodagoda, D. Liu and G. Dissanayake, “Electromyogram (EMG) Feature Reduction Using Mutual Components Analysis for Multifunction Prosthetic Fingers Control” , in Proc. Int. Conf. on Control, Automation, Robotics & Vision, Guangzhou, pp. 1534-1539, 2012.
[11] M. Khezri and M. Jahed, “A Neuro-Fuzzy Inference System for SEMGBased Identification of Hand Motion Commands”, IEEE Transactions on Industrial Electronics, vol. 58, no. 5, pp 1952-1960, 2011.
[12] L. J. Hargrove, K. Englehart and B. Hudgins, “A comparison of surface and intramuscular myoelectric signal classification”, IEEE Transactions on Biomedical Engineering, vol. 54 no. 5, pp. 847-853, 2007.
[13] K. Englehart, B. Hudgins, P. A. Parker, and M. Stevenson, “Classification of the myoelectric signal using time-frequency based representations,” Med. Eng. Phys., vol. 21, no. 6-7, pp. 431–438, 1999.
[14] R. N. Khushaba, S. Kodagoda, M.Takruri and G. Dissanayake, “Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals”, An Int. Journal of Expert System with Application, vol.39, no. 12, pp. 10731-38, 2012.
[15] A. Krogh A and J. Vedelsby, “Neural network ensembles, cross validation, and active learning”, In Advances in Neural Information Processing Systems 7, pages 231-238, MIT Press, 1995.
[16] B. Leo. “Bagging Predictors”, Journal of Machine Learning, vol. 24, no. 2, pp.123–140, 1996.
[17] C. A. C. Coello and R. L. Becerra, “Adding knowledge and efficient data structures to evolutionary programming: A cultural algorithm for constrained optimization”, Genetic and Evolutionary Computer Conerence, pp. 201-209, 2002.
[18] C. Tao, “SVM Ensemble Algorithm based on Bagging and CA” Int. Conf. on Mechanical Engineering and Automation, Advances in Biomedical Engineering, vol.10, pp. 389-393, 2012.
[19] H. Parvin, H. Alizadeh and B. Minaei, “A Modification on K-Nearest Neighbor Classifier”, Global Journal of Computer Science and Technology, vol.10 no. 14, pp.37, 2010.