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Estimation of Hand Gestures Using EMG and Bioimpedance

근전도와 임피던스를 이용한 손동작 추정

Kim, Soo-Chan
김수찬

  • Received : 2015.10.12
  • Accepted : 2015.12.04
  • Published : 2016.01.01

Abstract

EMG has specific information which is related to movements according to the activities of muscles. Therefore, users can intuitively control a prosthesis. For this reason, biosignals are very useful and convenient in this kind of application. Bioimpednace also provides specific information about movements like EMG. In this study, we used both EMG and bioimpedance to classify the typical hand gestures such as hand open, hand close, no motion (rest), supination, and pronation. Nine able-bodied subjects and one amputee were used as experimental data set. The accuracy was $98{\pm}1.9%$ when 2 bio-impedance and 8 EMG channels were used together for normal subjects. The number of EMG channels affected the accuracy, but it was stable when more than 5 channels were used. For the amputee, the accuracy is higher when we use both of them than when using only EMG. Therefore, accurate and stable hand motion estimation is possible by adding bioimepedance which shows structural information and EMG together.

Keywords

EMG;Bioimpedance;Prosthesis control;Amputee;Classification;Upper limb

References

  1. R. W. Wirta, D. R. Taylor, and F. R. Finley, "Patternrecognition arm prosthesis: a historical perspective-a final report," Bull Prosthet Res, pp. 8-35, 1978.
  2. Y. Hur, et al. "Development of a multifingered Myoelectric Prosthetic Hand with multifunctional expression," Institute of Control, Robotics and Systems Conf, pp.720-724, 2009.
  3. L. H. Smith, T.A. Kuiken, and L. J. Hargrove, "Realtime simultaneous and proportional myoelectric control using intramuscular EMG," J Neural Eng, vol. 11, No. 6(066013), 2014. https://doi.org/10.1088/1741-2560/11/6/066013
  4. 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. 236-57, 2007. https://doi.org/10.1080/03093640600994581
  5. S. H. Kim and et al, Disabled People Survey Report 2014, Korea Institute for Health and Social Affairs, Ministry of Health & Welfare
  6. S. H. Park, B. K. Hong, and M. S. Moon. "Development of the Wireless Myo-electric Hand Prosthesis Diagnoisis system to minimize malfunction and repair time," Rehabilitation Engineering and Assistive Technology Conf., pp.297-299, 2014.
  7. D. Farina, et al., "The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, No. 4, pp. 797-809, 2014. https://doi.org/10.1109/TNSRE.2014.2305111
  8. H. Farooq and S. Sharma, "A Review paper on EMG Signal and its Classification Techniques," 2015.
  9. R. F. Weir, et al., "Implantable myoelectric sensors (IMESs) for intramuscular electromyogram recording," IEEE Trans Biomed Eng, vol. 56, No. 1, pp. 159-71, 2009. https://doi.org/10.1109/TBME.2008.2005942
  10. T. Nakamura, et al., "Fundamental characteristics of human limb electrical impedance for biodynamic analysis," Med Biol Eng Comput, vol. 30, No. 5, pp. 465-72, 1992. https://doi.org/10.1007/BF02457823
  11. Kyle, U. G., et al., Bioelectrical impedance analysispart I: review of principles and methods. Clin Nutr, 2004. 23(5): p. 1226-43. https://doi.org/10.1016/j.clnu.2004.06.004
  12. S. C. Kim, et al., "Optimum electrode configuration for detection of arm movement using bio-impedance," Med Biol Eng Comput, 2003. vol. 41, No. 2, pp. 141-145. https://doi.org/10.1007/BF02344881
  13. Ottobock, http://www.ottobock-group.com/en/
  14. R. N. Khushaba, et al., "Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features," Neural Networks, vol. 55, pp. 42-58, 2014. https://doi.org/10.1016/j.neunet.2014.03.010
  15. A. Radmand, E. Scheme, and K. Englehart. "A characterization of the effect of limb position on EMG features to guide the development of effective prosthetic control schemes," 36th Annual International Conf. 2014.
  16. 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
  17. Texas Instruments, AFE4300 User's guide, http://www.ti.com/product/afe4300
  18. M. R. Masters, et al., "Towards better understanding and reducing the effect of limb position on myoelectric upper-limb prostheses," IEEE Engineering in Medicine and Biology Conf., pp.2577-2580, 2014.
  19. G. L. Firas AlOmari, "Analysis of Extracted Forearm sEMG Signal Using LDA, QDA, K-NN Classification Algorithms, "The Open Automation and Control Systems Journal, vol. 6, pp. 108-116, 2014. https://doi.org/10.2174/1874444301406010108
  20. G. Li and T. A. Kuiken, "EMG pattern recognition control of multifunctional prostheses by transradial amputees," IEEE Engineering in Medicine and Biology Conf., pp. 6914-7, 2009.
  21. A. Radmand, et al., "Investigation of optimum pattern recognition methods for robust myoelectric control during dynamic limb movement," Evaluation, vol. 1500, p.12, 2013.
  22. E. Scheme, K. Biron, and K. Englehart. "Improving myoelectric pattern recognition positional robustness using advanced training protocols," IEEE Engineering in Medicine and Biology Conf., 2011.

Acknowledgement

Supported by : 한경대학교