Research on EMG segmentation algorithm and walking analysis based on signal envelope and integral electrical signal
- 59 Downloads
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.
KeywordsSurface electromyography Wavelet denoising Signal envelope Signal segmentation Gait analysis
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).
- 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
- 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)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
- 16.Gupta, R.: Analysis of Surface Electromyogram Signals Using Integrated Bispectrum. In: India international conference on information processing, pp. 1–5 (2016)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
- 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