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Discrete Dynamics in Nature and Society
Volume 2016, Article ID 6897030, 11 pages
http://dx.doi.org/10.1155/2016/6897030
Research Article

A Discrete-Time Algorithm for Stiffness Extraction from sEMG and Its Application in Antidisturbance Teleoperation

Peidong Liang,1 Chenguang Yang,2 Ning Wang,3 and Ruifeng Li1

1State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, China
2Zienkiewicz Centre for Computational Engineering, Swansea University, Swansea SA1 8EN, UK
3Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong

Received 5 October 2015; Revised 10 January 2016; Accepted 11 January 2016

Academic Editor: Alicia Cordero

Copyright © 2016 Peidong Liang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. E. Burdet, R. Osu, D. W. Franklin, T. E. Milner, and M. Kawato, “The central nervous system stabilizes unstable dynamics by learning optimal impedance,” Nature, vol. 414, no. 6862, pp. 446–449, 2001. View at Publisher · View at Google Scholar · View at Scopus
  2. E. Burdet, G. Ganesh, C. Yang, and A. Albu-Schäffer, “Interaction force, impedance and trajectory adaptation: by humans, for robots,” in Experimental Robotics, O. Khatib, V. Kumar, and G. Sukhatme, Eds., vol. 29 of Tracts in Advanced Robotics, pp. 331–345, Springer, Berlin, Germany, 2014. View at Google Scholar
  3. C. Yang, G. Ganesh, S. Haddadin, S. Parusel, A. Albu-Schäeffer, and E. Burdet, “Human-like adaptation of force and impedance in stable and unstable interactions,” IEEE Transactions on Robotics, vol. 27, no. 5, pp. 918–930, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. C. Yang, Z. Li, and E. Burdet, “Human like learning algorithm for simultaneous force control and haptic identification,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '13), pp. 710–715, November 2013.
  5. B. Xu, “Robust adaptive neural control of flexible hypersonic flight vehicle with dead-zone input nonlinearity,” Nonlinear Dynamics, vol. 80, no. 3, pp. 1509–1520, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. B. Xu and Y. Zhang, “Neural discrete back-stepping control of hypersonic flight vehicle with equivalent prediction model,” Neurocomputing, vol. 154, pp. 337–346, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. B. Xu, Y. Pan, D. Wang, and F. Sun, “Discrete-time hypersonic flight control based on extreme learning machine,” Neurocomputing, vol. 128, pp. 232–241, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. G. A. Pratt and M. M. Williamson, “Series elastic actuators,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems 95. “Human Robot Interaction and Cooperative Robots”, vol. 1, pp. 399–406, Pittsburgh, Pa, USA, August 1995. View at Publisher · View at Google Scholar
  9. B. Vanderborght, A. Albu-Schaeffer, A. Bicchi et al., “Variable impedance actuators: a review,” Robotics and Autonomous Systems, vol. 61, no. 12, pp. 1601–1614, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. F. Ficuciello, L. Villani, and B. Siciliano, “Variable impedance control of redundant manipulators for intuitive human-robot physical interaction,” IEEE Transactions on Robotics, vol. 31, no. 4, pp. 850–863, 2015. View at Publisher · View at Google Scholar
  11. M. Li, H. Yin, K. Tahara, and A. Billard, “Learning object-level impedance control for robust grasping and dexterous manipulation,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '14), pp. 6784–6791, IEEE, Hong Kong, China, May 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Howard, D. J. Braun, and S. Vijayakumar, “Transferring human impedance behavior to heterogeneous variable impedance actuators,” IEEE Transactions on Robotics, vol. 29, no. 4, pp. 847–862, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. W. Song, X. Guo, F. Jiang, S. Yang, G. Jiang, and Y. Shi, “Teleoperation humanoid robot control system based on kinect sensor,” in Proceedings of the 4th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC '12), vol. 2, pp. 264–267, Nanchang, China, August 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. C. Stanton, A. Bogdanovych, and E. Ratanasena, “Teleoperation of a humanoid robot using full-body motion capture, example movements, and machine learning,” in Proceedings of the Australasian Conference on Robotics and Automation (ACRA '12), Wellington, New Zealand, December 2012.
  15. D. Kruse, J. T. Wen, and R. J. Radke, “A sensor-based dual-arm tele-robotic system,” IEEE Transactions on Automation Science and Engineering, vol. 12, no. 1, pp. 4–18, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Buchli, F. Stulp, E. Theodorou, and S. Schaal, “Learning variable impedance control,” International Journal of Robotics Research, vol. 30, no. 7, pp. 820–833, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Suphi Erden and A. Billard, “End-point impedance measurements at human hand during interactive manual welding with robot,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '14), pp. 126–133, Hong Kong, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. E. Gribovskaya, A. Kheddar, and A. Billard, “Motion learning and adaptive impedance for robot control during physical interaction with humans,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '11), pp. 4326–4332, Shanghai, China, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. K. Kronander and A. Billard, “Online learning of varying stiffness through physical human-robot interaction,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '12), pp. 1842–1849, May 2012.
  20. R. Osu, D. W. Franklin, H. Kato et al., “Short- and long-term changes in joint co-contraction associated with motor learning as revealed from surface EMG,” The Journal of Neurophysiology, vol. 88, no. 2, pp. 991–1004, 2002. View at Google Scholar · View at Scopus
  21. G. C. Ray and S. K. Guha, “Relationship between the surface e.m.g. and muscular force,” Medical and Biological Engineering and Computing, vol. 21, no. 5, pp. 579–586, 1983. View at Publisher · View at Google Scholar · View at Scopus
  22. J. Vogel, C. Castellini, and P. van der Smagt, “EMG-based teleoperation and manipulation with the DLR LWR-III,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '11), pp. 672–678, San Francisco, Calif, USA, September 2011. View at Publisher · View at Google Scholar
  23. K. You, K. Rhee, and H. Shin, “Finger motion decoding using emg signals corresponding various arm postures,” Experimental Neurobiology, vol. 19, no. 1, pp. 54–61, 2010. View at Publisher · View at Google Scholar
  24. J. W. Yoo, D. R. Lee, Y. J. Sim, J. H. You, and C. J. Kim, “Effects of innovative virtual reality game and EMG biofeedback on neuromotor control in cerebral palsy,” Bio-Medical Materials and Engineering, vol. 24, no. 6, pp. 3613–3618, 2014. View at Publisher · View at Google Scholar · View at Scopus
  25. G. F. Volk, M. Finkensieper, and O. Guntinas-Lichius, “EMG biofeedback training at home for patient with chronic facial palsy and defective healing,” Laryngo-Rhino-Otologie, vol. 93, no. 1, pp. 15–24, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. A. Ajoudani, M. Gabiccini, N. Tsagarakis, A. Albu-Schaffer, and A. Bicchi, “Teleimpedance: exploring the role of common-mode and configuration-dependant stiffness,” in Proceedings of the 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids '12), pp. 363–369, Osaka, Japan, November-December 2012.
  27. N. Wang, C. Yang, M. R. Lyu, and Z. Li, “An EMG enhanced impedance and force control framework for telerobot operation in space,” in Proceedings of the IEEE Aerospace Conference, pp. 1–10, Big Sky, Mont, USA, March 2014. View at Publisher · View at Google Scholar · View at Scopus
  28. P. Artemiadis, “EMG-based robot control interfaces: past, present and future,” Advances in Robotics & Automation, vol. 1, article e107, 2012. View at Google Scholar
  29. N. Karavas, A. Ajoudani, N. Tsagarakis, J. Saglia, A. Bicchi, and D. Caldwell, “Tele-impedance based stiffness and motion augmentation for a knee exoskeleton device,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '13), pp. 2194–2200, IEEE, Karlsruhe, Germany, May 2013. View at Publisher · View at Google Scholar · View at Scopus
  30. P. Liang, C. Yang, N. Wang, Z. Li, R. Li, and E. Burdet, “Implementation and test of human-operated and human-like adaptive impedance controls on baxter robot,” in Advances in Autonomous Robotics Systems, pp. 109–119, Springer, 2014. View at Google Scholar
  31. M. B. I. Reaz, M. S. Hussain, and F. Mohd-Yasin, “Techniques of EMG signal analysis: detection, processing, classification and applications,” Biological Procedures Online, vol. 8, no. 1, pp. 11–35, 2006. View at Publisher · View at Google Scholar · View at Scopus
  32. H. Gomi and M. Kawato, “Human arm stiffness and equilibrium-point trajectory during multi-joint movement,” Biological Cybernetics, vol. 76, no. 3, pp. 163–171, 1997. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  33. W. Gallagher, D. Gao, and J. Ueda, “Improved stability of haptic human-robot interfaces using measurement of human arm stiffness,” Advanced Robotics, vol. 28, no. 13, pp. 869–882, 2014. View at Publisher · View at Google Scholar · View at Scopus
  34. K. P. Tee, E. Burdet, C. M. Chew, and T. E. Milner, “A model of force and impedance in human arm movements,” Biological Cybernetics, vol. 90, no. 5, pp. 368–375, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  35. Baxter robot profile, 2014, http://www.rethinkrobotics.com/products/baxter/.
  36. R. Osu and H. Gomi, “Multijoint muscle regulation mechanisms examined by measured human arm stiffness and EMG signals,” Journal of Neurophysiology, vol. 81, no. 4, pp. 1458–1468, 1999. View at Google Scholar · View at Scopus
  37. K. Shamaei, G. S. Sawicki, and A. M. Dollar, “Estimation of quasi-stiffness of the human hip in the stance phase of walking,” PLoS ONE, vol. 8, no. 12, Article ID e81841, 2013. View at Publisher · View at Google Scholar · View at Scopus
  38. MYO, Myo profile, https://www.thalmic.com/en/myo/.
  39. ATI Mini45, 2015, http://www.ati-ia.com/products/ft/ft_models.aspx?id=Mini45.
  40. MATLAB, (R2014), The MathWorks, Natick, Mass, USA, 2014.
  41. S. D. Nandedkar, D. B. Sanders, E. V. Stälberg, and S. Andreassen, “Simulation of concentric needle EMG motor unit action potentials,” Muscle & Nerve, vol. 11, no. 2, pp. 151–159, 1988. View at Publisher · View at Google Scholar · View at Scopus
  42. R. Merletti and P. Di Torino, “Standards for reporting emg data,” Journal of Electromyography and Kinesiology, vol. 9, no. 1, pp. 3–4, 1999. View at Google Scholar
  43. O. Barzilay and A. Wolf, “A fast implementation for EMG signal linear envelope computation,” Journal of Electromyography and Kinesiology, vol. 21, no. 4, pp. 678–682, 2011. View at Publisher · View at Google Scholar · View at Scopus
  44. Squaring and lowpass filtering, February 2015, http://uk.mathworks.com/help/dsp/examples/envelope-detection.html.
  45. P. Maragos, J. F. Kaiser, and T. F. Quatieri, “Energy separation in signal modulations with application to speech analysis,” IEEE Transactions on Signal Processing, vol. 41, no. 10, pp. 3024–3051, 1993. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  46. Upper arm muscle profile, 2014, http://anthropotomy.com/muscles/.