Algorithm of Myoelectric Signals Processing for the Control of Prosthetic Robotic Hands

  • Rodrigo E. Russo Facultad de Ingeniería, Universidad Nacional de Mar del Plata, Mar del Plata, 7600, Argentina
  • Juana G. Fernández Facultad de Ingeniería, Universidad Nacional de Mar del Plata, Mar del Plata, 7600, Argentina
  • Raúl R. Rivera Facultad de Ingeniería, Universidad Nacional de Mar del Plata, Mar del Plata, 7600, Argentina
Keywords: EMG, Prosthesis, Robotic, Arduino, Hand, Bionic

Abstract

The development of robotic hand prosthetic aims to give back people with disabilities, the ability to recover the functionality needed to manipulate the objects of their daily environment. The electrical signals sent by the brain through the nervous system are associated with the type of movement that the limbs must execute. Myoelectric sensors are non-intrusive devices that allow the capture of electrical signals from the peripheral nervous system. The relationship between the signals originated in the brain tending to generate an action and the myoelectric ones as a result of them, are weakly correlated. For this reason, it is necessary to study their interaction in order to develop the algorithms that allow recognizing orders and transform them into commands that activate the corresponding movements of the prosthesis.
The present work shows the development of a prosthesis based on the design of an artificial hand Open Bionics to produce the movements, the MyoWare Muscle sensor for the capture of myoelectric signals (EMG) and the algorithm that allows to identify orders associated with three types of movement. Arduino Nano module performs the acquisition and control processes to meet the size and consumption requirements of this application.

Downloads

Download data is not yet available.

References

[1] B. Hudgins, P. Parker, and R. N. Scott, “A new strategy for multifunction myoelectric control”, IEEE Transactions on Biomedical Engineering, vol. 40, pp. 82–94, 1993.
[2] J. Brazeiro, S. Petraccia, M.Valdés. Mano controlada por señales musculares. BSc Thesis, Universidad de la República, 2015.
[3] R.G. Clement, K.E. Bugler , C.W. Oliver. “Bionic prosthetic hands: A review of present technology and future aspirations”. The Surgeon. vol. 9, pp. 336-340,2011.
[4] A.E. Schultz, T.A. Kuiken. “Neural interfaces for control of upper limb prostheses-the state of the art and future possibilities”. American Academy of Physical Medicine and Rehabilitation. vol.3, pp. 55-67,2011.
[5] M. Hakonena, H. Piitulainenb, A. Visala. “Current state of digital signal processing in myoelectric interfaces and related applications”. Biomedical Signal Processing and Control. vol. 18, pp. 334-359, 2015.
[6] M. A. Oskoei, H. Huosheng. “Myoelectric control systems a survey”. Biomedical Signal Processing and Control. Vol.2, pp. 275-294, 2011.
[7] A. Phinyomark, S. Hirunviriya, C. Limsakul, and P. Phukpattaranont, “Evaluation of emg feature extraction for hand movement recognition based on euclidean distance and standard deviation”, International Conference on Electrical Engineering /Electronics Computer Telecommunications and Information Technology (ECTI-CON), pp. 856–860, 2010.
[8] A. Phinyomark, P. Phukpattaranont, and C. Limsakul, “Feature reduction and selection for emg signal classification,” Expert Systems with Applications, vol. 39, no. 8, pp. 7420–7431, 2012.
[9] P. Azaripasand, A. Maleki, and A. Fallah, “Classification of adls using muscle activation waveform versus thirteen emg features”, 22ndIranian Conference on Biomedical Engineering (ICBME), pp. 189– 193, 2015.
[10] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Van- derplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python”, Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
[11] E. Jones, T. Oliphant, P. Peterson, et al., “SciPy: Open source scientific tools for Python.” 2001. Available at:
http://www.scipy.org/ Accessed on 2017-10-9.
[12] V. Zschorlich, “Digital filtering of emg-signals”, Electromyography and Clinical Neurophysiology , vol. 29, pp. 81–86, 1989.
[13] C. Cortes, V. Vapnik, “Support-vector networks”, Machine Learning, vol. 20, pp. 273–297, 1995.
[14] “Support vector machines.” Available at: http://scikit-learn.org/ stable/modules/svm.html. Accessed on 2017-10-17.
[15] “Nearest neighbors.” Available at: http://scikit-learn.org/ stable/modules/neighbors.html. Accessed on 2017-10-17.
[16] “Neural networks models (supervised).” Available at: http://scikit-learn.org/ stable/modules/neural_ networks_supervised.html. Accessed on 2017-10-17.
[17] “Prótesis Mano Robótica”. Available at: https://www.youtube.com/watch?v=-85fNRu4yp8&feature=youtu.be
Published
2018-04-25
How to Cite
Russo, R. E., Fernández, J. G., & Rivera, R. R. (2018). Algorithm of Myoelectric Signals Processing for the Control of Prosthetic Robotic Hands. Journal of Computer Science and Technology, 18(01), e04. https://doi.org/https://doi.org/10.24215/16666038.18.e04
Section
Original Articles