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Research on EMG segmentation algorithm and walking analysis based on signal envelope and integral electrical signal

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Abstract

Surface electromyography (SEMG) is an important tool for analyzing gait movements. Effective segmentation of electromyography (EMG) start/end points is an important step in the analysis of EMG signals. This paper presents a SEMG segmentation algorithm based on signal envelope and integral electromyography. Compared with manual segmentation, the coincidence rate is more than 90%. There is no statistical difference in the characteristic parameters of EMG signals calculated from the start/end points obtained by the segmentation algorithm and the manual segmentation method (P > 0.05). Based on this segmentation algorithm, quantitative analysis and comparison of the force situation of the iliopsoas, musculus gracilis, soleus and tibialis anterior muscles during the complete gait cycle are performed. This study lays the foundation for the application of surface electromyography in the field of rehabilitation analysis and control, such as rehabilitation training and rehabilitation robots.

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References

  1. Patla, A.E.: Effects of walking on various inclines on EMG patterns of lower limb muscles in humans. Hum. Mov. Sci. 5(4), 345–357 (1986)

    Article  Google Scholar 

  2. Chen, Yang, Zhao, Xingang, Han, Jianda: Hierarchical projection regression for online estimation of elbow joint angle using EMG signals. Neural Comput. Appl. 23(3–4), 1129–1138 (2013)

    Article  Google Scholar 

  3. Cha, Y.-J., Kim, J.-D., Choi, Y.-R., et al.: Effects of gait training with auditory feedback on walking and balancing ability in adults after hemiplegic stroke: a preliminary, randomized, controlled study. Int. J. Rehabil. Res. 41, 239–243 (2018)

    Google Scholar 

  4. Li, Yantao, Zhou, Gang, Graham, Daniel, Holtzhauer, Andrew: Towards an EEG-based brain-computer interface for online robot control. Multimed. Tools Appl. 75(13), 7999–8017 (2016)

    Article  Google Scholar 

  5. De Luca, A., Vernetti, H., Capra, C., et al.: Recovery and compensation after robotic assisted gait training in chronic stroke survivors. Disabil. Rehabil. Assist. Technol. (2018). https://doi.org/10.1080/17483107.2018.1466926

    Google Scholar 

  6. Staudenmann, D., Roeleveld, K., Stegeman, D., et al.: Methodological aspects of SEMG recordings for force estimation—a tutorial and review. J. Electromyogr. Kinesiol. 20(3), 375–387 (2010)

    Article  Google Scholar 

  7. Yoo, J.H., Nixon, M.S., Harris, C.J.: Extraction and description of moving human body by periodic motion analysis. In: Proceedings of ISCA 17th International Conference on Computers and Their Applications 2002, April 4–6, San Francisco, CA, pp. 110–113 (2002)

  8. Song, H., Cho, S., You, K.J., et al.: Improving DOA estimation and preventing target split using automotive radar sensor arrays. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 101(3), 590–594 (2018)

    Article  Google Scholar 

  9. Kubota, S., Nakata, Y., Eguchi, K., et al.: Feasibility of rehabilitation training with a newly developed wearable robot for patients with limited mobility. Arch. Phys. Med. Rehabil. 94(6), 1080–1087 (2013)

    Article  Google Scholar 

  10. Costa, A., Itkonen, M., Yamasaki, H., et al.: A novel approach to the segmentation of sEMG data based on the activation and deactivation of muscle synergies during movement. IEEE Robot. Autom. Lett. PP(99), 1 (2018)

    Google Scholar 

  11. Lin, L., Jianhui, W., et al.: Improved automatic segmentation method of sEMG based on signals’ energy value. Comput. Sci. 40(6a), 188–191 (2013). (in Chinese)

    Google Scholar 

  12. Wang, J., Jin, X., et al.: sEMG signal analysis method and its application research. China Sport Sci Technol 36(8), 26–28 (2000). (in Chinese)

    Google Scholar 

  13. Piskorowski, J.: Time-efficient removal of power-line noise from EMG signals using IIR notch filters with non-zero initial conditions. Biocybern. Biomed. Eng. 33(3), 171–178 (2013)

    Article  Google Scholar 

  14. Sayadi, O., Shamsollahi, M.B.: Multiadaptive bionic wavelet transform: application to ECG denoising and baseline wandering reduction. EURASIP J. Adv. Signal Process. 2007(1), 041274 (2007)

    Article  MATH  Google Scholar 

  15. Barzilay, O., Wolf, A.: A fast implementation for EMG signal linear envelope computation. J. Electromyogr. Kinesiol. Off. J. Int. Soc. Electrophysiol. Kinesiol. 21(4), 678 (2011)

    Article  Google Scholar 

  16. Gupta, R.: Analysis of Surface Electromyogram Signals Using Integrated Bispectrum. In: India international conference on information processing, pp. 1–5 (2016)

  17. Albulbul, A.: Evaluating major electrode types for idle biological signal measurements for modern medical technology. Bioengineering 3(3), 20 (2016)

    Article  Google Scholar 

  18. Qian, W.: Surface Electromyography Based Human Gait Analysis and Its Applications. University of Science and Technology of China, ‎Hefei (2013). (in Chinese)

    Google Scholar 

  19. Jang, E.H., Chi, S.Y., Lee, J.Y., et al.: Surface electromyogram activities of upper and lower limb muscles during walking. Int. J. Psychophysiol. 81(3), 341 (2011)

    Article  Google Scholar 

  20. Whittle, M.W.: Gait analysis: an introduction—3rd edition. Physiotherapy 77(11), 786 (2003)

    Google Scholar 

  21. Sabzevari, V.R., Jafari, A.H., Boostani, R.: Muscle synergy extraction during arm reaching movements at different speeds. Technol. Health Care Off. J. Eur. Soc. Eng. Med. 25(1), 123–136 (2016)

    Google Scholar 

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Acknowledgements

The project has been supported by the Special Fund for the Development of Shenzhen (China) Strategic New Industry (JCYJ20170818085946418) and the Shenzhen (China) Science and Technology Research and Development Fund (JCYJ20170306092000960).

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Correspondence to Xin’an Wang.

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Wang, M., Wang, X., Peng, C. et al. Research on EMG segmentation algorithm and walking analysis based on signal envelope and integral electrical signal. Photon Netw Commun 37, 195–203 (2019). https://doi.org/10.1007/s11107-018-0809-1

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  • DOI: https://doi.org/10.1007/s11107-018-0809-1

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