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Photonic Network Communications

, Volume 37, Issue 2, pp 195–203 | Cite as

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

  • Mo Wang
  • Xin’an WangEmail author
  • Chen Peng
  • Sixu Zhang
  • Zhuochen Fan
  • Zhong Liu
Original Paper

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.

Keywords

Surface electromyography Wavelet denoising Signal envelope Signal segmentation Gait analysis 

Notes

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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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Mo Wang
    • 1
  • Xin’an Wang
    • 1
    Email author
  • Chen Peng
    • 1
  • Sixu Zhang
    • 1
  • Zhuochen Fan
    • 1
  • Zhong Liu
    • 1
  1. 1.The Key Laboratory of Integrated Micro-systems Science and Engineering ApplicationsPeking University Shenzhen Graduate SchoolShenzhenPeople’s Republic of China

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