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Journal of Sensors
Volume 2017, Article ID 7360953, 18 pages
https://doi.org/10.1155/2017/7360953
Research Article

Generating Human-Like Velocity-Adapted Jumping Gait from sEMG Signals for Bionic Leg’s Control

1School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
2Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, China
3National Key Laboratory of Aerospace Flight Dynamics, School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China
4Images, Signals and Intelligence Systems Laboratory (LISSI/EA 3956), Senart-Fontainebleau Institute of Technology, UPEC, Bât. A, 77127 Lieusaint, France

Correspondence should be addressed to Weiwei Yu; nc.ude.upwn@iewiewuy

Received 11 March 2017; Revised 5 June 2017; Accepted 4 July 2017; Published 29 August 2017

Academic Editor: Xiaokun Zhang

Copyright © 2017 Weiwei Yu 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. M. Pang, S. Guo, Q. Huang, H. Ishihara, and H. Hirata, “Electromyography-based quantitative representation method for upper-limb elbow joint angle in sagittal plane,” Journal of Medical and Biological Engineering, vol. 35, no. 2, pp. 165–177, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. D. P. Yang, L. Jiang, Q. Huang, R. Liu, and H. Liu, “Experimental study of an EMG-controlled 5-DOF anthropomorphic prosthetic hand for motion restoration,” Journal of Intelligent and Robotic Systems: Theory and Applications, vol. 76, no. 3-4, pp. 427–441, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Ison and P. Artemiadis, “Proportional myoelectric control of robots: muscle synergy development drives performance enhancement, retainment, and generalization,” IEEE Transactions on Robotics, vol. 31, no. 2, pp. 259–268, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Hakonen, H. Piitulainen, and 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. View at Publisher · View at Google Scholar · View at Scopus
  5. A. d'Avella, P. Saltiel, and E. Bizzi, “Combinations of muscle synergies in the construction of a natural motor behavior,” Nature Neuroscience, vol. 6, no. 3, pp. 300–308, 2003. View at Publisher · View at Google Scholar · View at Scopus
  6. A. d'Avella and E. Bizzi, “Shared and specific muscle synergies in natural motor behaviors,” Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 8, pp. 3076–3081, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. N. Kuppuswamy and C. M. Harris, “Do muscle synergies reduce the dimensionality of behavior?” Frontiers in Computational Neuroscience, vol. 8, article no. 63, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Berniker, A. Jarc, E. Bizzi, and M. C. Tresch, “Simplified and effective motor control based on muscle synergies to exploit musculoskeletal dynamics,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 18, pp. 7601–7606, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. C. Alessandro, I. Delis, F. Nori, S. Panzeri, and B. Berret, “Muscle synergies in neuroscience and robotics: from input-space to task-space perspectives,” Frontiers in Computational Neuroscience, vol. 7, no. 43, pp. 1–16, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. G. Cappellini, Y. P. Ivanenko, R. E. Poppele, and F. Lacquaniti, “Motor patterns in human walking and running,” Journal of Neurophysiology, vol. 95, no. 6, pp. 3426–3437, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. C. C. Raasch and F. E. Zajac, “Locomotor strategy for pedaling: muscle groups and biomechanical functions,” Journal of Neurophysiology, vol. 82, no. 2, pp. 515–525, 1999. View at Google Scholar · View at Scopus
  12. S. A. Chvatal, G. Torres-Oviedo, S. A. Safavynia, and L. H. Ting, “Common muscle synergies for control of center of mass and force in nonstepping and stepping postural behaviors,” Journal of Neurophysiology, vol. 106, no. 2, pp. 999–1015, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. J. L. Allen and R. R. Neptune, “Three-dimensional modular control of human walking,” Journal of Biomechanics, vol. 45, no. 12, pp. 2157–2163, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. D. J. Clark, L. H. Ting, F. E. Zajac, R. R. Neptune, and S. A. Kautz, “Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke,” Journal of Neurophysiology, vol. 103, no. 2, pp. 844–857, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. E. J. Weiss and M. Flanders, “Muscular and postural synergies of the human hand,” Journal of Neurophysiology, vol. 92, no. 1, pp. 523–535, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. P. K. Artemiadis and K. J. Kyriakopoulos, “EMG-based control of a robot arm using low-dimensional embeddings,” IEEE Transactions on Robotics, vol. 26, no. 2, pp. 393–398, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Muceli, N. Jiang, and D. Farina, “Extracting signals robust to electrode number and shift for online simultaneous and proportional myoelectric control by factorization algorithms,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, no. 3, pp. 623–633, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. J. M. Hahne, F. Biebmann, N. Jiang et al., “Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, no. 2, pp. 269–279, 2014. View at Publisher · View at Google Scholar
  19. H. Kalani, S. Moghimi, and A. Akbarzadeh, “SEMG-based prediction of masticatory kinematics in rhythmic clenching movements,” Biomedical Signal Processing and Control, vol. 20, pp. 24–34, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. S. Muceli and D. Farina, “Simultaneous and proportional estimation of hand kinematics from EMG during mirrored movements at multiple degrees-of-freedom,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 20, no. 3, pp. 371–378, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. M. Ison and P. Artemiadis, “The role of muscle synergies in myoelectric control: Trends and challenges for simultaneous multifunction control,” Journal of Neural Engineering, vol. 11, no. 5, Article ID 051001, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. J. L. McKay and L. H. Ting, “Optimization of muscle activity for task-level goals predicts complex changes in limb forces across biomechanical contexts,” PLoS Computational Biology, vol. 8, no. 4, Article ID e1002465, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. J. J. Kutch and F. J. Valero-Cuevas, “Challenges and new approaches to proving the existence of muscle synergies of neural origin,” PLoS Computational Biology, vol. 8, no. 5, Article ID e1002434, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. M. C. Tresch, V. C. K. Cheung, and A. d'Avella, “Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets,” Journal of Neurophysiology, vol. 95, no. 4, pp. 2199–2212, 2006. View at Publisher · View at Google Scholar · View at Scopus
  25. C. Castellini and P. Van Der Smagt, “Evidence of muscle synergies during human grasping,” Biological Cybernetics, vol. 107, no. 2, pp. 233–245, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. A. B. Ajiboye and R. F. Weir, “Muscle synergies as a predictive framework for the EMG patterns of new hand postures,” Journal of Neural Engineering, vol. 6, no. 3, Article ID 036004, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. X. Chen, X. Zhu, and D. Zhang, “A discriminant bispectrum feature for surface electromyogram signal classification,” Medical Engineering and Physics, vol. 32, no. 2, pp. 126–135, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. J.-U. Chu, I. Moon, S.-K. Kim, and M.-S. Mun, “Control of multifunction myoelectric hand using a real-time EMG pattern recognition,” in Proceedings of the IEEE IRS/RSJ International Conference on Intelligent Robots and Systems, IROS 2005, pp. 3957–3962, can, August 2005. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Russo, M. D'Andola, A. Portone, F. Lacquaniti, and A. d'Avella, “Dimensionality of joint torques and muscle patterns for reaching,” Frontiers in Computational Neuroscience, vol. 8, article no. 24, 2014. View at Publisher · View at Google Scholar · View at Scopus
  30. C. Sabourin, W. Yu, and K. Madani, “Gait pattern based on CMAC neural network for robotic applications,” Neural Processing Letters, vol. 38, no. 2, pp. 261–279, 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. W. Yu, Y. Feng, W. Liang, R. Wang, and K. Madani, “Estimate the kinematics with EMG signal using fuzzy wavelet neural network for biomechanical leg application,” in Advances in Swarm Intelligence, vol. 9713 of Lecture Notes in Computer Science, pp. 132–140, Springer International Publishing, 2016. View at Publisher · View at Google Scholar
  32. S. Rapoport, J. Mizrahi, E. Kimmel, O. Verbitsky, and E. Isakov, “Constant and variable stiffness and damping of the leg joints in human hopping,” Journal of Biomechanical Engineering, vol. 125, no. 4, pp. 507–514, 2003. View at Publisher · View at Google Scholar · View at Scopus
  33. A. de Rugy, G. E. Loeb, and T. J. Carroll, “Are muscle synergies useful for neural control?” Frontiers in Computational Neuroscience, 2013. View at Publisher · View at Google Scholar · View at Scopus