The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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Development of Multi-DoFs Prosthetic Forearm based on EMG Pattern Recognition and Classification

근전도 패턴 인식 및 분류 기반 다자유도 전완 의수 개발

  • Lee, Seulah (Department of Electrical and Electronic Engineering, Hanyang University) ;
  • Choi, Yuna (Department of Electrical and Electronic Engineering, Hanyang University) ;
  • Yang, Sedong (Department of Electrical and Electronic Engineering, Hanyang University) ;
  • Hong, Geun Young (Department of Electrical and Electronic Engineering, Hanyang University) ;
  • Choi, Youngjin (Department of Electrical and Electronic Engineering, Hanyang University)
  • Received : 2019.07.12
  • Accepted : 2019.07.29
  • Published : 2019.08.30
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Abstract

This paper presents a multiple DoFs (degrees-of-freedom) prosthetic forearm and sEMG (surface electromyogram) pattern recognition and motion intent classification of forearm amputee. The developed prosthetic forearm has 9 DoFs hand and single-DoF wrist, and the socket is designed considering wearability. In addition, the pattern recognition based on sEMG is proposed for prosthetic control. Several experiments were conducted to substantiate the performance of the prosthetic forearm. First, the developed prosthetic forearm could perform various motions required for activity of daily living of forearm amputee. It was able to control according to shape and size of the object. Additionally, the amputee was able to perform 'tying up shoe' using the prosthetic forearm. Secondly, pattern recognition and classification experiments using the sEMG signals were performed to find out whether it could classify the motions according to the user's intents. For this purpose, sEMG signals were applied to the multilayer perceptron (MLP) for training and testing. As a result, overall classification accuracy arrived at 99.6% for all participants, and all the postures showed more than 97% accuracy.

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References

  1. K. Ziegler-Graham, E. J. MacKenzie, P. L. Ephraim, T. G. Travison, and R. Brookmeyer, "Estimating the prevalence of limb loss in the United States: 2005 to 2050," Archives of physicalmedicine and rehabilitation, vol. 89, no. 3, pp. 422-429, 2008. https://doi.org/10.1016/j.apmr.2007.11.005
  2. E. A. Biddiss and T. T, Chau, "Upper limb prosthesis use and abandonment: a survey of the last 25 years," Prosthetics and orthotics international, vol. 31, no. 3, pp. 236-257, 2007. https://doi.org/10.1080/03093640600994581
  3. C. W. Moran, "Revolutionizing prosthetics 2009 modular prosthetic limb-body interface: Overview of the prosthetic socket development," Johns Hopkins APL Technical Digest, vol. 30, no. 3, pp. 250-255, 2011.
  4. N. V. Iqbal and K. Subramaniam, "A review on upper-limb myoelectric prosthetic control," IETE Journal of Research, vol. 64, no. 6, pp. 740-752, 2018. https://doi.org/10.1080/03772063.2017.1381047
  5. M. A. Oskoei and H. Hu, "Myoelectric control systems-A survey," Biomedical signal processing and control, vol. 2, no. 4, pp. 275-294, 2007. https://doi.org/10.1016/j.bspc.2007.07.009
  6. F. Lunardini, C. Casellato, A. d'Avella, T. D. Sanger, and A. Pedrocchi, "Robustness and reliability of synergy-based myocontrol of a multiple degree of freedom robotic arm," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 9, pp. 940-950, 2016. https://doi.org/10.1109/TNSRE.2015.2483375
  7. S. Lee, M.-O. Kim, T. Kang, J. Park, and Y. Choi, "Knit Band Sensor for Myoelectric Control of Surface EMG-based Prosthetic Hand," IEEE Sensors Journal, vol. 18, no. 20, Oct., 2018.
  8. S. Pancholi and A. M. Joshi, "Electromyography-Based Hand Gesture Recognition System for Upper Limb Amputees," IEEE Sensors Letters, vol. 3, no. 3, pp. 1-4, 2019.
  9. J. U. Chu, L. Moon, and M. S. Mun, "A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand," IEEE Transactions on biomedical engineering, vol. 53, no. 11, pp. 2232-2239, 2006. https://doi.org/10.1109/TBME.2006.883695
  10. A. A. M. Lima, R. M. Araujo, F. A. G. dos Santos, V. H. Yoshizumi, F. K. de Barros, D. H. Spatti, D. H., and M. E. Dajer, "Classification of Hand Movements from EMG Signals using Optimized MLP," In IEEE 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, pp. 1-7, 2018.
  11. U. Cote-Allard, C. L. Fall, A. Drouin, A. Campeau-Lecours, C. Gosselin, K. Glette, F. Laviolette, and B. Gosselin, "Deep learning for electromyographic hand gesture signal classification using transfer learning," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 4, pp. 760-771. 2019. https://doi.org/10.1109/TNSRE.2019.2896269
  12. D. A. Neumann, Kinesiology of the musculoskeleta, 3rd ed. Elsevier Health Science, 2017, ch. 8 Hand, p. 256.
  13. Y. Lianfa, C. Jiating, and Z. Zhifang, "Design of a novel knee prosthesis mechanism with good stability," Int. Rob. Auto. J., vol. 4, no. 4, pp. 278-284, 2018.
  14. P. A. Halverson, A. E. Bowden, and L. L. Howell, "A compliant-mechanism approach to achieving specific quality of motion in a lumbar total disc replacement," Int. J. spine surgery, vol. 6, pp. 78-86, 2012. https://doi.org/10.1016/j.ijsp.2012.02.002
  15. Y.-J. Kim, "Anthropomorphic low-inertia high-stiffness manipulator for high-speed safe interaction," IEEE Trans. on Robotics, vol. 33, no. 6, pp. 1358-1374, 2017. https://doi.org/10.1109/TRO.2017.2732354
  16. R. Hecht-Nielsen, "Theory of the backpropagation neural network," Neural networks for perception. Academic Press, pp. 65-93, 1992.
  17. A. Phinyomark, P. Phukpattaranont, and C. Limsakul. "Feature reduction and selection for EMG signal classification," Expert systems with applications, vol. 39, no. 8, pp. 7420-7431, 2012. https://doi.org/10.1016/j.eswa.2012.01.102

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