Métodos Computacionales para el Reconocimiento de Patrones Mioeléctricos en el Control de Exoesqueletos Robóticos: una Revisión

Autores/as

  • Alberto López Delis Universidad de Oriente, Cuba
  • Andrés F. Ruiz Olaya Universidad Antonio Nariño

Palabras clave:

electromiografía de superficie, control mioléctrico, extracción de características, reconocimiento de patrones

Resumen

Traditionally, human-machine interfaces have been a widely studied research topic in the rehabilitation field. In order to empower the physical rehabilitation processes of motor disabled people, there are growing efforts within the scientific community aimed at developing new robotic devices such as exoskeletons. Myoelectric control is an advanced technique concerning with detection, processing, classification and application of electromyography signals to control external rehabilitation systems and devices. In physical therapies using robotic systems it is fundamental an effective identification of the human motion to command these systems. In literature, surface EMG signals have been widely used, taking into account that they can reflect the movement intention. This work provides a revision of the computational techniques and methods that have been used in literature, based on features extraction and pattern recognition techniques aimed at myoelectric control of robotic exoskeletons. It is considered researches that have used these methods to control robotic devices, and it is presented future trends in this research field.

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Citas

H.S. Lo, S.Q. Xie, “Exoskeleton robots for upper-limb rehabilitation: State of the art and future prospect”, Medical Engineering & Physics, 2012, Vol. 34, No. 3, p.p. 261-268.

C J. De Luca, Electromyography. Encyclopedia of Medical Devices and Instrumentation: Editorial John Wiley, 2006.

M. A. Oskoei y H. Hu, “Myoelectric control systems — A survey”, Biomedical Signal Processing and Control, vol. 2, pp. 275-294, 2007.

J.L. Pons, Wearable Robots, Biomechatronic Exoskeletons: Editorial John Wiley, 2008.

K. Englehart, B. Hudgins y P. Parker, Intelligent Systems and Technologies in Rehabilitation Engineering: Editorial CRC Press, 2001.

M. Zecca, S. Micera, M.C. Carroza, y P. Dario, “Control of multifunctional prosthetic hands

by processing the electromyoagraphic signal”, Critical Reviews in Biomedical Engineering, vol. 30(4-6), pp. 459-485, 2002.

P. Angkoon, P. Pornchai y L. Chusak, “Feature reduction and selection for EMG signal classification”, Expert Systems with Applications, vol. 39, pp. 7420-7431, 2012.

R. Merletti, A. Botter, A. Troiano, E. Merlo y M. A. Minetto, “Technology and instrumentation for detection and conditioning of the surface electromyographic: State of the art”, Clinical Biomechanics, vol. 24, pp. 122-134, 2009.

K. Englehart y B. Hudgings, “A robust, real time control scheme for multifunction myoelectric control”, IEEE Trans. Biomedical Eng., vol. 50(7), pp. 848-854, 2003.

K. Englehart, B.Hudgins y P. A. Parker, “A wavelet-based continuous classification scheme for multifunction control”, IEEE Trans. Biomedical Eng., vol. 48(3), pp. 302-310, 2001.

R. Merletti y P. Parker, Electromyography Physiology, Engineering and Noninvasive Applications”: Editorial IEEE Press Engineering in Medicine and Biology Society, 2004.

M. Zardoshti-Kermani, B.C. Wheeler, K. Badie y R.M. Hashemi, “EMG feature evaluation for movement control of upper extremity prostheses”, IEEE Transactions on Rehabilitation Engineering, vol. 3(4), pp. 324-333, 1995.

M. Lei, Z. Wang y Z. Feng, “Detecting nonlinearity of action surface EMG signal”, Physics Letters A, 2001, vol. 290(5-6), pp. 297-303.

H.P. Huang y C.Y. Chen, “Development of a myoelectric discrimination system for a multi-degree prosthetic hand”, en Proc. of IEEE International Conference on Robotics and Automation vol. 3, pp. 2392–2397, 1999.

R. Merletti, “Standards for reporting EMG data”, Journal of Electromyography and Kinesiology, vol. 6(1), III-IV, 1996.

B. Hudgins, P. Parker y R. Scott, “A new strategy for multifunction myoelectric control”, IEEE Transactions on Biomedical Engineering, vol. 40(1), pp. 82–94, 1993.

M. A. Oskoei y H. Hu, “Support vector machine based classification scheme for myoelectric control applied to upper limb”, IEEE Transactions on Biomedical Engineering, vol. 55(8), pp. 1956–1965, 2008.

R. Boostani y H. Moradi, “Evaluation of the forearm EMG signal features for the control of a prosthetic hand”, Physiological Measurement, vol. 24(2), pp. 309–319, 2003.

A. Fougner, Proportional myoelectric control of a multifunction upper limb prosthesis: Tesis de Maestría, Norwegian University of Science and Technology, Trondheim, Norway, 2007.

S. Karlsson, J. Yu y M. Akay,”Enhancement of spectral analysis of myoelectric signals during static contractions using wavelet methods”, IEEE Trans. Biomed. Eng., vol. 46(6), pp. 670–684, 1999.

S. V. Vaseghi, Advanced Digital Signal Processing and Noise Reduction: Editorial Wiley, 2000.

E. C. Ifeachor y B. W. Jervis, Digital Signal Processing: A Practical Approach: Editorial Addison-Wesley, 1993.

S. Ferguson y G. R. Dunlop, “Grasp Recognition from Myoelectric Signals“, Proc. Australian Conference on Robotics and Automation, pp. 83-87, 2002.

A. Phinyomark, Ch. Limsakul Ch. y P. Phukpattaranont, “A Comparative Study of Wavelet Denoising for Multifunction Myoelectric Control”, Proc. International Conference on Computer and Automation Engineering, pp. 21-25, 2009.

V. Vapnik, The Nature of Statistical Learning Theory: Editorial Springer, 1995.

T. Takagi y M. Sugeno, “Fuzzy Identification of Systems and its Applications to Modeling and Control”, IEEE Trans. Systems, Man and Cibernetics, vol15, pp. 116-132, 1985.

R.A.R.C. Gopuray K. Kiguchi, “Electromyography (EMG)-signal based fuzzy-neuro control of a 3 degrees of freedom (3DO F) exoskeleton robot for human upper-limb motion assist”, Journal of the National Science Foundation of SriLanka, vol. 37(4), pp. 241-248, 2009.

H-J. Liu y K-Y. Young, “An Adaptive UpperArm EMG-Based Robot Control System”, International Journal of Fuzzy Systems, Vol. 12(3), pp. 181-189, 2010.

G. W. Favieiro y A. Balbinot, “Adaptive Neuro-Fuzzy Logic Analysis Based on Myoelectric Signals for Multifunction Prosthesis Control”, Proc. 33rd Annual International Conference of the IEEE EMBS, 2011, pp. 7888-7891.

J-S. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System”, IEEE Tran. Systems, Man and Cibernetics, vol. 23, pp. 665-685, 1996.

M. DiCicco, L. Lucas, Y. Matsuoka, “Comparison of Control Strategies for an EMG Controlled Orthotic Exoskeleton for the Hand”, Proceedings of the 2004 IEEE International Conference on Robotics & Automation, p.p. 1622- 1627.

M. Mulas, M. Folgheraiter, G. Gini, “An EMG-controlled Exoskeleton for Hand Rehabilitation”, Proceedings of the 2005 IEEE 9th International Conference on Rehabilitation Robotics, p.p. 371-374.

A. Wege, A. Zimmermann, “Electromyography Sensor Based Control for a Hand Exoskeleton, Proceedings of the 2007 IEEE Int. Conf. on Robotics and Biomimetics, p.p. 1470-1475.

Z.O. Khokhar, Z.G. Xiao, C.Menon, “Surface EMG pattern recognition for real-time control of a wrist exoskeleton”, BioMedical Engineering OnLine 2010, 9:41.

N.S.K. Ho, K.Y. Tong, X.L. Hu, K.L. Fung, X.J. Wei, W. Rong, and E.A. Susanto, “An EMGdriven Exoskeleton Hand Robotic Training Device on Chronic Stroke Subjects”, in Proc. IEEE Int. Conf. on Rehabilitation Robotics (ICORR), 2011, pp. 1 – 5.

Kazuo Kiguchi, Takakazu Tanaka, and Toshio Fukuda, “Neuro-Fuzzy Control of a Robotic Exoskeleton with EMG Signals”, IEEE Transactions on Fuzzy Systems Vol. 12, Issue 4, 2004, pp. 481 – 490.

M.A. Mikulski, “Electromyogram Control Algorithms for the Upper Limb Single-DOF Powered Exoskeleton”, in Proc. IEEE Int. Conf. on Human System Interactions (HSI), 2011, pp. 117 – 122.

R. Latif, S. Sanei, and K. Nazarpour, “Classification of elbow electromyography signals based on directed transfer functions”, in Proc. IEEE Int. Conf. on BioMedical Engineering and Informatics, 2008, pp. 371 – 374.

D. S. Andreasen, S.K. Allen, and D.A.Backus, “Exoskeleton with EMG Based Active Assistance for Rehabilitation”, in Proc. IEEE Int. Conf. on Rehabilitation Robotics, 2005, pp. 333 – 336.

R.A.R.C. Gopura and Kazuo Kiguchi, “Application of Surface Electromyographic Signals to Control Exoskeleton Robots”, en Applications of EMG in Clinical and Sports Medicine, 2012, Dr. Catriona Steele (Ed.), ISBN: 978-953-307-798-7.

J.M. Ochoa, M. Listenberger, D.G. Kamper, and S. Wook Lee, “Use of an Electromyographically Driven Hand Orthosis for Training after Stroke”, in Proc. IEEE Int. Conf. on Rehabilitation Robotics (ICORR), 2011, pp. 1 – 5.

A.C. Tsai, J.J. Luh, and T.T. Lin, “A Modified Multi-Channel EMG Feature for Upper Limb Motion Pattern Recognition”, 34th Annual International Conference of the IEEE EMBS, 2012, pp. 3596-3599.

T. Ando, M. Watanabe, K. Nishimoto, Y. Matsumoto, M. Seki, and M.G. Fujie, “Myoelectric-Controlled Exoskeletal Elbow Robot to Suppress Essential Tremor: Extraction of Elbow Flexion Movement Using STFTs and TDNN”, Journal of Robotics and Mechatronics, Vol. 24, No. 1, pp. 141-149, 2012.

D.P. Ferris, K.E. Gordon, G.S. Sawicki, A. Peethambaran, “An improved powered ankle–foot orthosis using proportional myoelectric control”, Gait & Posture 23 (2006) 425–428.

C. Fleischer and G. Hommel, “A Human–Exoskeleton Interface Utilizing Electromyography”, IEEE Transactions on Robotics, 2008, Vol. 24, No. 4, p.p. 872-882.

H. He and K. Kiguchi, “A Study on EMGBased Control of Exoskeleton Robots for Human Lower-limb Motion Assist”, in Proc. IEEE Int. Conf. on Information Technology Applications in Biomedicine, 2007, pp. 292 – 295.

Hui Yan, Ray P.S. Han, Yu Wang and JieruChi, “Controlling a Powered Exoskeleton System via Electromyographic Signals”, Proceedings of the 2009 IEEE International Conference on Robotics and Biomimetics, 2009, p.p. 349-353.

Z. Zhang, J. Jiang, L. Peng, and H. Fan, “A Method to Control Ankle Exoskeleton with Surface Electromyography Signals”, In proceeding of: Intelligent Robotics and Applications - Third International Conference, ICIRA 2010, Shanghai, China, November 10-12, 2010, p.p. 390-397.

J.F. Veneman, R. Kruidhof, E.E.G. Hekman, R. Ekkelenkamp, E.H.F. Van Asseldonk, and H. van der Kooij, “Design and Evaluation of the LOPES Exoskeleton Robot for Interactive Gait Rehabilitation”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 15, Issue 3, 2007, pp. 379 – 386.

E. Ceseracciu, M. Reggiani, Z. Sawacha, M. Sartori, F. Spolaor, C. Cobelli, E. Pagell, “SVM classification of locomotion modes using surface electromyography for applications in rehabilitation robotics”, RO-MAN, 2010 IEEE, pp. 165 –170.

Feng Zhang, Pengfeng Li, Zeng-Guang Hou, Yixiong Chen, Fei Xu, Jin Hu, Qingling Li, Min Tan, “SEMG Feature Extraction Methods for Pattern Recognition of Upper Limbs”, in Proc. IEEE Int. Conf. on Advanced Mechatronic Systems (ICAMechS), 2011, pp. 222 – 227.

X. Navarro, T.B. Krueger, N. Lago, S. Micera, T. Stieglitz, P. Dario, “A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems, Journal of Peripheral Nervous Systems, 2005 Sep;10(3):229-58.

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2013-09-09
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Cómo citar

López Delis, A., & Ruiz Olaya, A. F. (2013). Métodos Computacionales para el Reconocimiento de Patrones Mioeléctricos en el Control de Exoesqueletos Robóticos: una Revisión. INGE@UAN - TENDENCIAS EN LA INGENIERÍA, 3(5). Recuperado a partir de https://revistas.uan.edu.co/index.php/ingeuan/article/view/350

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Artículo de investigación científica y tecnológica

Métrica