The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
권호 표지
DOI QR코드

DOI QR Code

Wrist and Grasping Forces Estimation using Electromyography for Robotic Prosthesis

근전도 신호를 이용한 손목 힘 및 악력 추정

  • Received : 2017.01.13
  • Accepted : 2017.04.26
  • Published : 2017.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes a method to simultaneously estimate two degrees of freedom in wrist forces (extension - flexion, adduction - abduction) and one degree of freedom in grasping forces using Electromyography (EMG) signals of the forearms. To correlate the EMG signals with the forces, we applied a multi - layer perceptron(MLP), which is a machine learning method, and used the characteristics of the muscles constituting the forearm to generate learning data. Through the experiments, the similarity between the MLP target value and the estimated value was investigated by applying the coefficient of determination ($R^2$) and root mean square error (RMSE) to evaluate the performance of the proposed method. As a result, the $R^2$ values with respect to the wrist flexion-extension, adduction - abduction and grasping forces were 0.79, 0.73 and 0.78 and RMSE were 0.12, 0.17, 0.13 respectively.

Keywords

References

  1. R.W. Wirta, D.R. Taylor, and F.R. Finley, "Patternrecognition arm prosthesis: a historical perspective-a final report," Moss Rehabilitation Hospital, Philadelphia, USA, Fall, 1978.
  2. E.A. Biddiss and T.T. Chau, "Upper limb prosthesis use and abandonment: A survey of the last 25 years," Prosthet. Orthot. Int., vol. 31, no. 3, pp. 236-257, Sep, 2007. https://doi.org/10.1080/03093640600994581
  3. P.A. Parker and R.N. Scott, "Myoelectric control of prostheses," CRC Crit. Rev. Biomed. Eng., vol. 13, no. 4, pp. 283-310, Jan, 1986.
  4. RSL Steeper, BeBionic Product Brochure, [Online], http://bebionic.com/uploads/files/RSLLIT383_-_bebio nic _TriFold_ROW_web.pdf, Accessed: April 27, 2017.
  5. Touch Bionics, Touch Bionics Product Brochure, [Online], http://www.touchbionics.com/resources/document-library, Accessed: April 27, 2017.
  6. S.-H. Park, S.-P. Lee, "EMG pattern recognition based on artificial intelligence techniques," IEEE Trans. Rehab. Eng., vol. 6, no.4, pp. 400-405, Dec, 1998. https://doi.org/10.1109/86.736154
  7. S.-J. Kim, E.-C. Jeong, S.-M. Lee, and Y.-R. Song, "Improvements of Multi- Wrist Movements", Trans. Korean Institute of Electrical Engineers, vol. 61, no. 5, pp. 757-762, May, 2012. https://doi.org/10.5370/KIEE.2012.61.5.757
  8. Z.O. Khokhar, Z.G. Xiao, and C. Menon et al., "Surface emg pattern recognition for real-time control of a wrist exoskeleton", Biomedical engineering online, vol. 9, no. 1, pp. 41, 2010. https://doi.org/10.1186/1475-925X-9-41
  9. A. Hiraiwa, K. Shimohara, and Y. Tokunaga, "EMG pattern analysis and classification by neural network," Proc. IEEE Int. Conf. Systems Man Cybernetics, Cambridge, USA, pp. 1113-1115, 1989.
  10. D. Peleg, E. Braiman, E. Yom-Tov, and G. F. Inbar, "Classification of finger activation for use in a robotic prosthesis arm," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 10, no. 4, pp. 290-293, Dec, 2002. https://doi.org/10.1109/TNSRE.2002.806831
  11. F. Tenore, A. Ramos, A. Fahmy, S. Acharya, R. Etienne- Cummings, and N.V. Thakor, "Towards the control of individual fingers of a prosthetic hand using surface EMG signals," Proc. IEEE Eng. Med. Biol. Soc. Conf., Lyon, France, pp. 6145-6148, 2007.
  12. S. Muceli, D. Farina, "Simultaneous and proportional estimation of handkinematics from emg during mirrored movements at multipledegrees-of-freedom," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 20, no. 3, pp. 371-378, May, 2012. https://doi.org/10.1109/TNSRE.2011.2178039
  13. N. Jiang, J.L. Vest-Nielsen, S. Muceli, D. Farina, "EMGbased simultaneous and proportional estimation of wrist/ hand dynamics in uni-lateral trans-radial amputees," J. Neuroeng. Rehabil., vol. 9, no. 1, pp. 42, Jun, 2012. https://doi.org/10.1186/1743-0003-9-42
  14. L. Pan, X. Sheng, D. Zhang, and X. Zhu, "Simultaneous and Proportional Estimation of Finger Joint Angles from Surface EMG Signals during Mirrored Bilateral Movements", Int. Conf. Intell. Robots Applicat, Busan, South Korea, pp. 493-499, 2013.
  15. N. Jiang, K.B. Englehart, and P.A. Parker, "Extracting simultaneous and proportional neural control information for multiple-DOF prostheses from the surface electromyographic signal," IEEE Trans. Biomed. Eng., vol. 56, no. 4, pp. 1070-1080, Apr, 2009. https://doi.org/10.1109/TBME.2008.2007967
  16. R. Hecht-Nielsen, "Theory of the backpropagation neural network," Proc. IEEE Int. Joint Conf. Neural Networks, Washington, USA, pp. 593-605, 1989.
  17. A. Phinyomark, P. Phukpattaranont, and C. Limsakul, "Feature reduction and selection for EMG signal classification," Expert Systems With Applicat., vol. 39, no. 8, pp. 7420-7431, 2012. https://doi.org/10.1016/j.eswa.2012.01.102
  18. J. Sola and J. Sevilla, "Importance of input data normalization for the application of neural networks to complex industrial problems," IEEE Trans. Nucl. Sci., vol. 44, no. 3, pp. 1464-1468, Jun, 1997. https://doi.org/10.1109/23.589532
  19. R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," Proc. 14th Int. Joint Conf. Artificial Intelligence, Montreal, Canada, pp. 338-345, 1995.
  20. H.U. Kuriki, F.M.d. Azevedo, L.S.O. Takahashi, E.M. Mello, R.d.F.N. Filho, and N. Alves, "The relationship between electromyography and muscle force," EMG Methods for Evaluating Muscle and Nerve Function, 1th ed. InTech, 2011, ch. 3, pp. 31-54.
  21. J.-H. Bae, S.-W. Park, J.-H. Park, M.-H. Baeg, D. Kim, and S.-R. Oh, "Development of a low cost anthropomorphic robot hand with high capability," Proc. IEEE/ RSJ Int. Conf. Intell. Robots Syst., Vilamoura, Portugal, pp. 4776-4782, 2012.
  22. D.-H. Lee, J.-H. Park, S.-W. Park, M.-H Baeg, and J.-H Bae, "KITECH-Hand: A Highly Dexterous and Modularized Robotic Hand", IEEE/ASME Trans. Mechatron., vol. 22, no. 2, pp. 876-887, Apr, 2017. https://doi.org/10.1109/TMECH.2016.2634602
  23. Y.-J. Kim, YOUTUBE, [Online], https://youtu.be/FxTP0gNn7yk, Accessed: April 27, 2017.

Cited by

  1. 의수 제어용 동작 인식을 위한 웨어러블 밴드 센서 vol.13, pp.4, 2017, https://doi.org/10.7746/jkros.2018.13.4.265
  2. 무학습 근전도 패턴 인식 알고리즘: 부분 수부 절단 환자 사례 연구 vol.14, pp.3, 2017, https://doi.org/10.7746/jkros.2019.14.3.211
  3. 원형 구조 알고리즘을 이용한 근전도 패턴 인식 및 분류 vol.15, pp.1, 2020, https://doi.org/10.7746/jkros.2020.15.1.062
  4. Remote Control of Mobile Robot Using Electromyogram-based Hand Gesture Recognition vol.30, pp.5, 2020, https://doi.org/10.5050/ksnve.2020.30.5.497
  5. Forearm Muscle Activity Patterns during Open Thyroidectomy Using 8-Channel Surface Electromyography vol.21, pp.1, 2017, https://doi.org/10.16956/jes.2021.21.1.27