We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

EMG-Controlled Transradial Prostheses - An Investigation into Machine Learning Techniques

by Mufassir Abdur Rahim, Ghulam Rasool, Nasir Ahmad
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 3
Year of Publication: 2017
Authors: Mufassir Abdur Rahim, Ghulam Rasool, Nasir Ahmad
10.5120/ijca2017915354

Mufassir Abdur Rahim, Ghulam Rasool, Nasir Ahmad . EMG-Controlled Transradial Prostheses - An Investigation into Machine Learning Techniques. International Journal of Computer Applications. 174, 3 ( Sep 2017), 1-8. DOI=10.5120/ijca2017915354

@article{ 10.5120/ijca2017915354,
author = { Mufassir Abdur Rahim, Ghulam Rasool, Nasir Ahmad },
title = { EMG-Controlled Transradial Prostheses - An Investigation into Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2017 },
volume = { 174 },
number = { 3 },
month = { Sep },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume174/number3/28384-2017915354/ },
doi = { 10.5120/ijca2017915354 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:21:08.806801+05:30
%A Mufassir Abdur Rahim
%A Ghulam Rasool
%A Nasir Ahmad
%T EMG-Controlled Transradial Prostheses - An Investigation into Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 3
%P 1-8
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The electromyogram (EMG) signals recorded from the surface of skeletal muscles are stochastic in nature and exhibit repeatable patterns for similar muscle activations. Therefore, machine learning algorithms can be used to learn their patterns and identify the movement intent even in the absence of an actual limb. The EMG signals are recorded from the residual muscles/muscle sites after amputation (acquired or congenital) and a representative set of features is extracted. The feature data are passed on to a machine learning algorithm for training and later use in real-time for controlling a prosthetic device. Numerous features of the EMG signal based on its amplitude, spectral contents, and stochastic nature have been proposed. Similarly, various dimensionality reduction techniques, as well as, classification algorithms have also been used. In this study, we provide in-depth analyses of different features of the EMG signals and classification algorithms along with the effect of dimensionality reduction on the classification accuracy. The surface EMG data recorded from the forearm muscles of twelve able-bodied volunteers was used to extract six different feature sets (fourteen individual features). The feature data with/without dimensionality reduction was used to train and test three different classification algorithms, i.e., the linear discriminant analysis (LDA), support vector machines (SVM), and artificial neural networks (ANN). Our extensive study showed that the feature set consisting of the EMG amplitude, spectral, and stochasticity information provided the highest classification accuracy with a linear classifier, i.e., the LDA.

References
  1. B. Afsharipour, M. Sandhu, G. Rasool, N. L. Suresh, and W. Z. Rymer. Identifying Spinal Lesion Site from Surface EMG Grid Recordings, pages 39–43. Springer International Publishing, Cham, 2017.
  2. H Albunashee, G Rasool, K Iqbal, and G White. A new technique to improve the operation of prosthetic limbs during muscle fatigue. Journal of the Arkansas Academy of Science, 70(1):35–39, 2016.
  3. Jun-Uk Chu and Yun-Jung Lee. Conjugate-prior-penalized learning of Gaussian mixture models for multifunction myoelectric hand control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 17(3):287-297, June 2009.
  4. A. L. Ciancio, F. Cordella, R. Barone, R. A. Romeo, A. D. Bellingegni, R. Sacchetti, A. Davalli, G. Di Pino, F. Ranieri, V. Di Lazzaro, E. Guglielmelli, and L. Zollo. Control of prosthetic hands via the peripheral nervous system. Front Neurosci, 10:116, 2016.
  5. C. Cipriani, C. Antfolk, M. Controzzi, G. Lundborg, B. Rosen, M.C. Carrozza, and F. Sebelius. Online myoelectric control of a dexterous hand prosthesis by transradial amputees. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(3):260-270, June 2011.
  6. F. Cordella, A. L. Ciancio, R. Sacchetti, A. Davalli, A. G. Cutti, E. Guglielmelli, and L. Zollo. Literature review on needs of upper limb prosthesis users. Front Neurosci, 10:209, 2016.
  7. Dario Farina, Ning Jiang, Hubertus Rehbaum, Ales Holobar, Bernhard Graimann, Hans Dietl, and Oskar C Aszmann. The extraction of neural information from the surface emg for the control of upper-limb prostheses: emerging avenues and challenges. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(4):797–809, 2014.
  8. Dario Farina, Roberto Merletti, and Roger M Enoka. The extraction of neural strategies from the surface emg. Journal of applied physiology, 96(4):1486–1495, 2004.
  9. P. J. Gallant, E. L. Morin, and L. E. Peppard. Feature-based classification of myoelectric signals using artificial neural networks. Medical & Biological Engineering & Computing, 36(4):485–489, 1998.
  10. Levi J. Hargrove, Guanglin Li, Kevin B. Englehart, and Bernard S. Hudgins. Principal components analysis preprocessing for improved classification accuracies in patternrecognition- based myoelectric control. IEEE Transactions on Biomedical Engineering, 56(5):1407–1414, 2009.
  11. Yonghong Huang, Kevin Englehart, Bernard Hudgins, and Adrian D C Chan. A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses. IEEE Transactions on Biomedical Engineering, 52(11):1801-1811, November 2005.
  12. Ning Jiang, Kevin Englehart, and Philip A Parker. Extracting simultaneous and proportional neural control information for multiple-DOF prostheses from the surface electromyographic signal. IEEE Transactions on Biomedical Engineering, 56(4):1070-1080, April 2009.
  13. Ning Jiang, Johnny LG Vest-Nielsen, Silvia Muceli, and Dario Farina. EMG-based simultaneous and proportional estimation of wrist/hand dynamics in uni-lateral trans-radial amputees. Journal of NeuroEngineering and Rehabilitation, 9(1):42, June 2012.
  14. Marie-Franoise Lucas, Adrien Gaufriau, Sylvain Pascual, Christian Doncarli, and Dario Farina. Multi-channel surface emg classification using support vector machines and signalbased wavelet optimization. Biomedical Signal Processing and Control, 3(2):169–174, 2008.
  15. Roberto Merletti and Philip Parker. Electromyography: Physiology, Engineering, and Noninvasive Applications. Wiley- IEEE Press, Hoboken, NJ, 2004.
  16. S. Micera, J. Carpaneto, and S. Raspopovic. Control of hand prostheses using peripheral information. IEEE Reviews in Biomedical Engineering, 3:48–68, 2010.
  17. Kaveh Momen, Sridhar Krishnan, and Tom Chau. Real-time classification of forearm electromyographic signals corresponding to user-selected intentional movements for multifunction prosthesis control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(4):535-542, December 2007.
  18. G. R. Naik, D. K. Kumar, and M. Palaniswami. Surface emg based hand gesture identification using semi blind ica: validation of ica matrix analysis. Electromyography and Clinical Neurophysiology, 48(3-4):169–180, 2008.
  19. G.R. Naik, D.K. Kumar, and Jayadeva. Twin SVM for gesture classification using the surface electromyogram. IEEE Transactions on Information Technology in Biomedicine, 14(2):301-308, March 2010.
  20. Max Ortiz-Catalan, Rickard Branemark, and Bo Hakansson. Biopatrec: A modular research platform for the control of artificial limbs based on pattern recognition algorithms. Source Code for Biology and Medicine, 8(11):1–18, 2013.
  21. Mohammadreza Asghari Oskoei and Huosheng Hu. Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Transactions on Biomedical Engineering, 55(8):1956–1965, 2008.
  22. M. B. I. Raez, M. S. Hussain, and F. Mohd-Yasin. Techniques of emg signal analysis: detection, processing, classification and applications. Biological Procedures Online, 8:11– 35, 2006.
  23. G. Rasool, B. Afsharipour, N. Suresh, and W. Z. Rymer. Spatial analysis of multichannel surface emg in hemiplegic stroke. IEEE Trans Neural Syst Rehabil Eng, 2017.
  24. G. Rasool, K. Iqbal, N. Bouaynaya, and G. White. Real-time task discrimination for myoelectric control employing taskspecific muscle synergies. IEEE Trans Neural Syst Rehabil Eng, 2015.
  25. Ghulam Rasool. Myoelectric prostheses: Novel methodologies for enhancing usability and control. Thesis, University of Arkansas at Little Rock, 2014.
  26. Ghulam Rasool, Babak Afsharipour, Nina L Suresh, Xiaogang Hu, and William Zev Rymer. Spatial analysis of muscular activations in stroke survivors. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pages 6058–6061. IEEE, 2015.
  27. Ghulam Rasool, Nidhal Bouaynaya, Kamran Iqbal, and Gannon White. Surface myoelectric signal classification using the AR-GARCH model. Biomedical Signal Processing and Control, 13:327–336, 2014.
  28. Erik Scheme and Kevin Englehart. Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use. The Journal of Rehabilitation Research and Development, 48(6):643–643, 2011.
  29. Erik J. Scheme, Kevin B. Englehart, and Bernard S. Hudgins. Selective classification for improved robustness of myoelectric control under nonideal conditions. IEEE Transactions on Biomedical Engineering, 58(6):1698–1705, 2011.
  30. J. W. Sensinger, B. A. Lock, and T. A. Kuiken. Adaptive pattern recognition of myoelectric signals: Exploration of conceptual framework and practical algorithms. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 17(3):270–278, 2009.
  31. Ann M Simon, Levi J Hargrove, Blair A Lock, and Todd A Kuiken. A decision-based velocity ramp for minimizing the effect of misclassifications during real-time pattern recognition control. IEEE Transactions on Biomedical Engineering, 58(8):2360–2368, 2011.
  32. Alan Smith, Pooja Nanda, and Jr. Brown, Edward E. Development of a myoelectric control scheme based on a time delayed neural network. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009:3004– 3007, 2009.
  33. Lauren H. Smith, Levi J. Hargrove, Blair A. Lock, and Todd A. Kuiken. Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification error and controller delay. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(2):186–192, 2011.
  34. Francesco V G Tenore, Ander Ramos, Amir Fahmy, Soumyadipta Acharya, Ralph Etienne-Cummings, and Nitish V Thakor. Decoding of individuated finger movements using surface electromyography. IEEE Transactions on Biomedical Engineering, 56(5):1427-1434, May 2009.
  35. Aaron J Young, Levi Hargrove, and Todd A. Kuiken. Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration. IEEE Transactions on Biomedical Engineering, 59(3):645-652, March 2012.
  36. M. Zecca, S. Micera, M. C. Carrozza, and P. Dario. Control of multifunctional prosthetic hands by processing the electromyographic signal. Critical Reviews in Biomedical Engineering, 30(4-6):459–485, 2002.
Index Terms

Computer Science
Information Sciences

Keywords

Electromyogram Prosthesis Linear Discriminant Analysis Support Vector Machines Artificial Neural Networks