Table of Contents Author Guidelines Submit a Manuscript
Journal of Sensors
Volume 2017 (2017), Article ID 3980906, 12 pages
https://doi.org/10.1155/2017/3980906
Research Article

An Evaluation of Hand-Force Prediction Using Artificial Neural-Network Regression Models of Surface EMG Signals for Handwear Devices

1Department of Computer Science and Communications Engineering, Waseda University, Tokyo, Japan
2Department of Electronic and Physical Systems, Waseda University, Tokyo, Japan

Correspondence should be addressed to Masayuki Yokoyama; pj.ca.adesaw.sc.balsi@amayokoy.ikuyasam

Received 27 April 2017; Revised 27 July 2017; Accepted 1 October 2017; Published 25 October 2017

Academic Editor: Ji Zhang

Copyright © 2017 Masayuki Yokoyama 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. T. Starner, “The challenges of wearable computing: part 1,” IEEE Micro, vol. 21, no. 4, pp. 44–52, 2001. View at Publisher · View at Google Scholar · View at Scopus
  2. T. Starner, “The challenges of wearable computing: Part 2,” IEEE Micro, vol. 21, no. 4, pp. 54–67, 2001. View at Publisher · View at Google Scholar · View at Scopus
  3. Z. Xu, C. Xiang, W.-H. Wang, J.-H. Yang, V. Lantz, and K.-Q. Wang, “Hand gesture recognition and virtual game control based on 3D accelerometer and EMG sensors,” in Proceedings of the 13th International Conference on Intelligent User Interfaces, IUI'09, pp. 401–405, usa, February 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. L. Oikonomidis, N. Kyriazis, and A. A. Argyros, “Efficient model-based 3D tracking of hand articulations using kinect,” in Proceedings of the Proc, pp. 101–11, 2011.
  5. J. Tompson, M. Stein, Y. Lecun, and K. Perlin, “Real-time continuous pose recovery of human hands using convolutional networks,” ACM Transactions on Graphics, vol. 33, no. 5, article no. 169, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. V. G. Popescu, G. C. Burdea, M. Bouzit, and V. R. Hentz, “A virtual-reality-based telerehabilitation system with force feedback,” IEEE Transactions on Information Technology in Biomedicine, vol. 4, no. 1, pp. 45–51, 2000. View at Publisher · View at Google Scholar · View at Scopus
  7. D. Jack, R. Boian, A. S. Merians et al., “Virtual reality-enhanced stroke rehabilitation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 9, no. 3, pp. 308–318, 2001. View at Publisher · View at Google Scholar · View at Scopus
  8. L. Dovat, O. Lambercy, R. Gassert et al., “HandCARE: A cable-actuated rehabilitation system to train hand function after stroke,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 16, no. 6, pp. 582–591, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. Z. Ma, P. B-Tzvi, and J. Danoff, “Hand rehabilitation learning system with an exoskeleton robotic glove,” IEEE Trans. Neural Syst. Rehabil. Eng, vol. 24, no. 12, pp. 1323–1332, 2016. View at Publisher · View at Google Scholar
  10. S. Demain, C. D. Metcalf, G. V. Merrett, D. Zheng, and S. Cunningham, “A narrative review on haptic devices: Relating the physiology and psychophysical properties of the hand to devices for rehabilitation in central nervous system disorders,” Disability and Rehabilitation: Assistive Technology, vol. 8, no. 3, pp. 181–189, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Mulas, M. Folgheraiter, and G. Gini, “An EMG-controlled exoskeleton for hand rehabilitation,” in Proceedings of the Proc. IEEE 9th Int. Conf. Rehabil. Robot, pp. 371–374, June 2005.
  12. N. S. K. Ho, K. Y. Tong, X. L. Hu et al., “An EMG-driven exoskeleton hand robotic training device on chronic stroke subjects: Task training system for stroke rehabilitation,” in Proceedings of the Proc. IEEE Int. Conf. Rehabil, p. 1, June 2011.
  13. Y. Y. Huang, K. H. Low, and H. B. Lim, “Initial analysis of EMG signals of hand functions associated to rehabilitation tasks,” in Proceedings of the Proc. IEEE Int. Conf. Robot, pp. 530–535, Biom. (ROBIO, 2009.
  14. D. Leonardis, M. Barsotti, C. Loconsole et al., “An EMG-controlled robotic hand exoskeleton for bilateral rehabilitation,” IEEE Transactions on Haptics, vol. 8, no. 2, pp. 140–151, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. M. J. M. Hoozemans and J. H. Van Dieën, “Prediction of handgrip forces using surface EMG of forearm muscles,” Journal of Electromyography & Kinesiology, vol. 15, no. 4, pp. 358–366, 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. T. S. Saponas, D. S. Tan, D. Morris, and R. Balakrishnan, “Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces,” in Proceedings of the 26th Annual CHI Conference on Human Factors in Computing Systems, CHI 2008, pp. 515–524, ita, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. A. A. Adewuyi, L. J. Hargrove, and T. A. Kuiken, “An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 4, pp. 485–494, 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. F. Mobasser and K. Hashtrudi-Zaad, “Hand force estimation using electromyography signals,” in Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 2631–2636, esp, April 2005. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Kasuya, M. Seki, K. Kawamura, Y. Kobayashi, M. G. Fujie, and H. Yokoi, “Robust grip force estimation under electric feedback using muscle stiffness and electromyography for powered prosthetic hand,” in Proceedings of the 2013 IEEE International Conference on Robotics and Automation, ICRA 2013, pp. 93–98, May 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. C. Choi, S. Kwon, W. Park, H.-D. Lee, and J. Kim, “Real-time pinch force estimation by surface electromyography using an artificial neural network,” Medical Engineering & Physics, vol. 32, no. 5, pp. 429–436, 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. H. Srinivasan, S. Gupta, W. Sheng, and H. Chen, “Estimation of hand force from surface electromyography signals using artificial neural network,” in Proceedings of the 10th World Congress on Intelligent Control and Automation, WCICA 2012, pp. 584–589, July 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. 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
  23. J.-Y. Baek, J.-H. An, J.-M. Choi, K.-S. Park, and S.-H. Lee, “Flexible polymetric dry electrodes for the long-term monitoring of ECG,” Sensors and Actuators A: Physical, vol. 143, pp. 423–429, 2008. View at Publisher · View at Google Scholar
  24. V. Marozas, A. Petrenas, S. Daukantas, and A. Lukosevicius, “A comparison of conductive textile-based and silver/silver chloride gel electrodes in exercise electrocardiogram recordings,” Journal of Electrocardiology, vol. 44, no. 2, pp. 189–194, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. A. Freivalds, “Tool evaluation and design,” in Occupational ergonomics: theory and applications, Marcel Dekkar Inc, A. Bhattacharya and J. McGlothlin, Eds., pp. 303–327, New York, 1996. View at Google Scholar
  26. A. O. Prerotto, Anatomical guide for the electromyographer: the limbs and trunk, Charls C Thomas Publisher Ltd, Springfield, Illinois, 5th edition, 2011.
  27. K. Y. Ang, Y. Y. Huang, and K. H. Low, “Electromyography analysis for pre-clinical trials of hand rehabilitation tasks using design of experiments,” in Proceedings of the Proc. IEEE Int. Conf.Mechatro, pp. 915–920, 2009.
  28. M. Yokoyama, R. Koyama, and M. Yanagisawa, “Muscle analysis of hand and forearm during tapping using surface electromyography,” in Proceedings of the 4th IEEE Global Conference on Consumer Electronics, GCCE 2015, pp. 595–598, jpn, October 2015. View at Publisher · View at Google Scholar · View at Scopus
  29. H. H. C. M. Savelberg and W. Herzog, “Prediction of dynamic tendon forces from electromyographic signals: An artificial neural network approach,” Journal of Neuroscience Methods, vol. 78, no. 1-2, pp. 65–74, 1997. View at Publisher · View at Google Scholar · View at Scopus
  30. M. M. Liu, W. Herzog, and H. H. C. M. Savelberg, “Dynamic muscle force predictions from EMG: An artificial neural network approach,” Journal of Electromyography & Kinesiology, vol. 9, no. 6, pp. 391–400, 1999. View at Publisher · View at Google Scholar · View at Scopus
  31. F. Mobasser and K. H-Zaad, “A Comparative approach to hand force estimation using artificial neural networks,” Biomedical Engineering and Computational Biology, vol. 4, p. 15, 2012. View at Google Scholar
  32. M. Vilimek, “An artificial neural network approach and sensitivity analysis in predicting skeletal muscle forces,” Acta of Bioengineering and Biomechanics, vol. 16, no. 3, pp. 119–127, 2014. View at Publisher · View at Google Scholar · View at Scopus
  33. G. Rayan and E. Akelman, The hand: anatomy, examination, and diagnosis, Wolters Kluwer Health, Philadelphia: Lippincott Williams, 4th edition, 2011.