single-rb.php

JRM Vol.24 No.4 pp. 585-594
doi: 10.20965/jrm.2012.p0585
(2012)

Paper:

Study on the sEMG Driven Upper Limb Exoskeleton Rehabilitation Device in Bilateral Rehabilitation

Muye Pang*, Shuxiang Guo**, ***, and Zhibin Song**

*Graduated School of Engineering, Kagawa University, 2217-20 Hayashi-cho, Takamatsu 761-0396, Japan

**Department of Intelligent Mechanical Systems Engineering, Kagawa University, 2217-20 Hayashi-cho, Takamatsu 761-0396, Japan

***College of Automation, Harbin Engineering University, 145 Nantong Street, Harbin, Heilongjiang, China

Received:
February 1, 2012
Accepted:
May 17, 2012
Published:
August 20, 2012
Keywords:
surface EMG, upper limb, bilateral rehabilitation, continuous control, neural network
Abstract

This study presents an implementation of a continuous upper limb motion recognition method based on surface electromyography (sEMG) into control of an Upper Limb Exoskeleton Rehabilitation Device (ULERD). The raw sEMG hardly can be used directly as a reference control signals due to various influences. A feature extraction method, namely, an autoregressive algorithm, was thus applied to extract features of sEMG. The features of sEMG are usually used as switching signals to indicate whether activation happens. In this study, a continuous recognition is implemented using the coefficients of an AR model because of the coefficients’ characteristics of fitting the trend in sEMG signals. Because of the low signal-to-noise ratio of sEMG, the optimal order of the AR model was calculated based on the Akaike Information Criterion for a good fit to raw signals. Back-propagation neural networks were then trained using coefficients to recognize motion. Recognition results were used as the control source for the rehabilitation device. Experimental results showed that this method is effective for obtaining a control source through raw sEMG signals derived fromthe unaffected arm for motor control of a ULERD equipped on the affected armduring bilateral rehabilitation in real-time.

Cite this article as:
Muye Pang, Shuxiang Guo, and Zhibin Song, “Study on the sEMG Driven Upper Limb Exoskeleton Rehabilitation Device in Bilateral Rehabilitation,” J. Robot. Mechatron., Vol.24, No.4, pp. 585-594, 2012.
Data files:
References
  1. [1] C. Butefisch, H. Hummelsheim, P. Denzler, and K. H. Mauritz, “Repetitive training of isolated movements improves the outcome of motor rehabilitation of the centrally paretic hand,” J. Neurologic. Sci., Vol.130, pp. 59-68, 1995.
  2. [2] E. Taub, N. E. Miller, and T. A. Novack, “Technique to improve chronic motor deficit after stroke,” Arch. Phys. Med. Rehab., Vol.74, pp. 347-354, 1993.
  3. [3] Q. Pan, S. Guo, and T. Okada, “A Novel Hybrid Wireless Microrobot,” Int. J. of Mechatronics and Automation, Vol.1, No.1, pp. 60-69, 2011.
  4. [4] S. M. M. Rahman, R. Ikeura, S. Hayakawa, and H. Sawai, “Design guidelines for power assist robots for lifting heavy objects considering weight perception, grasp differences and worst-cases,” Int. J. of Mechatronics and Automation, Vol.1, No.1, pp. 46-59, 2011.
  5. [5] Z. Song, S. Guo, and Y. Fu, “Development of an upper extremity motor function rehabilitation system and an assessment system,” Int. J. of Mechatronics and Automation, Vol.1, No.1, pp. 19-28, 2011.
  6. [6] T. Doi, R. Hodoshima, Y. Fukuda, S. Hirose, T. Okamoto, and J. Mori, “Development of Quadruped Walking Robot TITAN XI for Steep Slopes – Slope Map Generation and Map Information Application,” J. of Robotics and Mechatronics, Vol.18, No.3, pp. 318-324, 2006.
  7. [7] M. Nakashima, T. Tsubaki, and K. Ono, “Three-Dimensional Movement in Water of the Dolphin Robot – Control Between Two Positions by Roll and Pitch Combination,” J. of Robotics and Mechatronics, Vol.18, No.3, pp. 347-355, 2006.
  8. [8] T. Mori and K. Tsujioka, “Human-Like Daily Action Recognition Model,” J. of Robotics and Mechatronics, Vol.17, No.6, pp. 672-680, 2005.
  9. [9] Y. Nakamura, T. Mori, Y. Tokita, T. Shibata, and S. Ishii, “Off-Policy Natural Policy Gradient Method for a Biped Walking Using a CPG Controller,” J. of Robotics and Mechatronics, Vol.17, No.6, pp. 636-644, 2005.
  10. [10] P. S. Lum, C. G. Burgar, P. C. Shor, M. Majmundar, and M. V. der Loos, “Robot-Assisted Movement Training Compared With Conventional Therapy Techniques for the Rehabilitation of Upper-LimbMotor Function After Stroke,” Arch PhysMed Rehabilitation, Vol.83, pp. 952-959, 2002.
  11. [11] R. G. Carson, “Neural pathways mediating bilateral interactions between the upper limbs,” Brain Research Reviews, Vol.49, pp. 641-662, 2005.
  12. [12] R. J. Nudo, B. M. Wise, F. SiFuentes, and G. W. Milliken. “Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct,” Science, Vol.272, pp. 1792-1794, 1996.
  13. [13] M. K. Chan, R. K. Tong, and K. Y. Chung, “Bilateral upper limb training with functional electric stimulation in patients with chronic stroke,” Neurorehabilitation and Neural Repair, Vol.23, pp. 357-365, 2009.
  14. [14] P. K. Jamwal, S. Xie, and C. Aw. Kean, “Kinematic design optimization of parallel ankle rehabilitation robot using modified genetic algorithm,” Robotics and Autonomous Systems, Vol.57, Issue 10, pp. 1018-1027, 2009.
  15. [15] R. Ambar, M. S. Ahmad, and M. M. Abdul Jamil, “Design and Development of a Multi-sensor Monitoring Device for Arm Rehabilitation,” Int. J. of Integrated Engineering, Vol.3, No.2, pp. 55-62, 2011.
  16. [16] E. E. Cavallaro, J. Rosen, J. C. Perry, and S. Burns, “Real-TimeMyoprocessors for a Neural Controlled Powered Exoskeleton Arm,” IEEE Trans. on Biomedical Engineering, Vol.53, No.11, pp. 2387-2396, 2006.
  17. [17] D. Gagnon, N.Arjmand, A. Plamondon, A. Shirazi-Adl, and C. Lariviere, “An improved multi-joint EMG-assisted optimization approach to estimate joint and muscle forces in a musculoskeletal model of the lumbar spine,” J. of Biomechanics, Vol.44, Issue 8, pp. 1521-1529, 2011.
  18. [18] M. H. Sherif. “For Movement Pattern Recognition in Upper Limb Prostheses,” Ph.D. thesis, University of California at Los Angeles, 1980.
  19. [19] L. Lucas, M. DiCicco, and Y. Matsuoka, “An EMG-Controlled Hand Exoskeleton for Natural Pinching,” J. of Robotics and Mechatronics, Vol.16, No.5, pp. 482-488, 2004.
  20. [20] C. J. De Luca, “The Use of Surface Electromyography in Biomechanics,” J. of Applied Biomechanics, Vol.13, pp. 1-38, 1997.
  21. [21] D. Stashuk, “EMG signal decomposition: how can it be accomplished and used,” J. of Electromyography and Kinesiology, Vol.11, Issue 3, pp. 151-173.
  22. [22] Y. Amirat, K. Djouani, M. Kirad, and N. Saadia, “Neural Adaptive Approach-Application to Robot Force Control in an Unknown Environment,” J. of Robotics and Mechatronics, Vol.18, No.5, pp. 529-538, 2006.
  23. [23] M. B. I. Reaz, M. S. Hussain, and F. Mohd-Yasin, “Techniques of EMG signal analysis: detection, processing, classification and applications,” Biomedical and Life Sciences, Vol.8, pp. 11-35, 2006.
  24. [24] B. Daachi, A. Benallegue, T. Madani, and M. E. Daachi, “Neural Networks for Redundant Robot Manipulators Control with Obstacles Avoidance,” J. of Robotics and Mechatronics, Vol.16, No.1, pp. 90-96, 2004.
  25. [25] M. Otake and Y. Nakamura, “Spinal Information Processing and its Application to Motor Learning Support,” J. of Robotics and Mechatronics, Vol.17, No.6, pp. 617-627, 2005.
  26. [26] Z. Song and S. Guo, “Implementation of Self-rehabilitation for Upper Limb based on a Haptic Device and an Exoskeleton Device,” Proc. of the 2011 IEEE Int. Conf. on Mechatronics and Automation, pp. 1911-1916, 2011.
  27. [27] Z. Song and S. Guo, “Development of a Master-slave System for Upper Limb Rehabilitation,” The 5th Int. Conf. on Advanced Mechatronics, pp. 768-773, 2010.
  28. [28] M. F. Moller, “A scaled conjugate gradient algorithm for fast supervised learning,” Neural Networks, Vol.6, Issue 4, pp. 525-533, 1990.
  29. [29] A. Phinyomark, C. Limsakul, and P. Phukpattaranont, “A Novel Feature Extraction for Robust EMG Pattern Recognition,” J. of Computing, Vol.1, Issue 1, pp. 71-80, 2009.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Dec. 09, 2021