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Comparative Analysis of Surface Electromyography Features on Bilateral Upper Limbs for Stroke Evaluation: A Preliminary Study

  • Hongze Jiang
  • Yang Li
  • Yu Zhou
  • Honghai LiuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10984)

Abstract

The loss of upper limb functionality caused by stroke significantly influences patients daily living. Surface electromyography (sEMG) has been applied for study of stroke rehabilitation for tens of years. This paper is an attempt to evaluate stroke severity using sEMG. An experiment including four basic upper limb arm motions was carried out, with eleven able-bodied and six stroke patients being employed. Several sEMG features of bilateral upper limbs were compared for their relationship with stroke severity, and results showed that a new proposed feature named Envelope Correlation (EC) performed best. The experiment outcomes provided a prospect to evaluate stroke grade using sEMG.

Keywords

Surface electromyography (sEMG) sEMG features Stroke Evaluation Bilateral upper limbs 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 51575338, 51575407, 51475427) and the Fundamental Research Funds for the Central Universities (No. 17JCYB03).

References

  1. 1.
    Zhang, X., Zhou, P.: High-density myoelectric pattern recognition toward improved stroke rehabilitation. IEEE Trans. Biomed. Eng. 59(6), 1649–1657 (2012)CrossRefGoogle Scholar
  2. 2.
    Kim, M.S., et al.: The influence of laterality of pharyngeal bolus passage on dysphagia in hemiplegic stroke patients. Ann. Rehabil. Med. 36(5), 696–701 (2012)CrossRefGoogle Scholar
  3. 3.
    Kulishova, T.V., Shinkorenko, O.V.: The effectiveness of early rehabilitation of the patients presenting with ischemic stroke. Voprosy kurortologii, fizioterapii, i lechebnoi fizicheskoi kultury (6), 9–12 (2014). Vopr Kurortol Fizioter Lech Fiz KultGoogle Scholar
  4. 4.
    Rasmussen, R.S., et al.: Stroke rehabilitation at home before and after discharge reduced disability and improved quality of life: a randomised controlled trial. Clin. Rehabil. 30(3), 225–236 (2016)CrossRefGoogle Scholar
  5. 5.
    Jiang, R.-r., Yan, C.H.E.N., Pan, C.-h.: Advance in assessment of upper limb and hand motor function in patients after stroke. Chin. J. Rehabil. Theor. Pract. 10, 1173–1177 (2015)Google Scholar
  6. 6.
    Dalla Toffola, E.: Myoelectric manifestations of muscle changes in stroke patients. Arch. Phys. Med. Rehabil. 82(5), 661–665 (2001)CrossRefGoogle Scholar
  7. 7.
    Han, R., Ni, C.M.: Effect of electromygraphic biofeedback on upper extremity function in patients with hemiplegia after stroke. Zhongguo Kangfu Lilun yu Shijian 11(3), 209–210 (2005)Google Scholar
  8. 8.
    Cheng, P.-T., et al.: Leg muscle activation patterns of sit-to-stand movement in stroke patients. Am. J. Phys. Med. Rehabil. 83(1), 10–16 (2004)CrossRefGoogle Scholar
  9. 9.
    Chowdhury, R.H., et al.: Surface electromyography signal processing and classification techniques. Sensors 13(9), 12431–12466 (2013)CrossRefGoogle Scholar
  10. 10.
    Ahsan, M.R., Ibrahimy, M.I., Khalifa, O.O.: EMG signal classification for human computer interaction: a review. Eur. J. Sci. Res. 33(3), 480–501 (2009)Google Scholar
  11. 11.
    Hudgins, B., Parker, P., Scott, R.N.: A new strategy for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 40(1), 82–94 (1993)CrossRefGoogle Scholar
  12. 12.
    Englehart, K., Hudgins, B.: A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 50(7), 848–854 (2003)CrossRefGoogle Scholar
  13. 13.
    Alkan, A., Gnay, M.: Identification of EMG signals using discriminant analysis and SVM classifier. Expert Syst. Appl. 39(1), 44–47 (2012)CrossRefGoogle Scholar
  14. 14.
    Lin, K., et al.: A robust gesture recognition algorithm based on surface EMG. In: Seventh International Conference on Advanced Computational Intelligence (ICACI). IEEE (2015)Google Scholar
  15. 15.
    Kwatny, E., Thomas, D.H., Kwatny, H.G.: An application of signal processing techniques to the study of myoelectric signals. IEEE Trans. Biomed. Eng. 4, 303–313 (1970)CrossRefGoogle Scholar
  16. 16.
    Sekulic, D., Medved, V., Rausavljevi, N.: EMG analysis of muscle load during simulation of characteristic postures in dinghy sailing. J. Sports Med. Phys. Fit. 46(1), 20 (2006)Google Scholar
  17. 17.
    Oskoei, M.A., Huosheng, H.: Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans. Biomed. Eng. 55(8), 1956–1965 (2008)CrossRefGoogle Scholar
  18. 18.
    Ashby, P., Mailis, A., Hunter, J.: The evaluation of spasticity. Can. J. Neurol. Sci. 14(S3), 497–500 (1987)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Mechanical System and VibrationShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China
  2. 2.Shanghai Jing’an District Central HospitalShanghaiPeople’s Republic of China

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