Abstract
The assessment of muscle function has been an intractable issue in several fields of biomechanics, sports medicine and rehabilitation medicine. The surface EMG signals are often used to evaluate the muscle function. However, even for the muscles of the healthy hands, some characteristics of the surface EMG signals are also usually obviously different, such as the values of amplitude, mean spectral frequency, etc. It is difficult to use these values as the reference standards to assess the corresponding muscles. For this, this paper applies a kind of the nonlinear values-—the distribution rates of the symplectic geometry spectrum (SGS) method to analyze the surface EMG signals. By comparing the distribution rates of SGS between the original surface EMG signals and their surrogate data, the results show that the surface EMG signals are not from a random process but a nonlinear deterministic process. And the distribution rate value can be taken as a reference standard to analyze the surface EMG signals.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Al-Mulla, M.R., Sepulveda, F., Colley, M.: I A review of non-invasive techniques to detect and predict localized muscle fatigue. Sensor 11, 3545–3594 (2011)
Georgakis, A., Stergioulas, L.K., Giakas, G.: Fatigue Analysis of the Surface EMG Signal in Isometric Constant Force Contractions Using the Averaged Instantaneous Frequency. IEEE Trans. on Biomed. Eng. 50(2), 262–265 (2003)
Yang, Z., Liu, B., Peng, J., Ma, Z.: The Preliminary Nonlinear Dynamical Analysis of Surface Electromyogram. Space Med. & Med. Eng. 12(3), 185–187 (1999)
Gupta, V., Suryanarayanan, S., Reddy, N.P.: Fractal dimension of surface EMG during isokinetic contractions. In: 17th IEEE Annual Conference on Engineering in Medicine and Biology Society, vol. 2, pp. 1331–1332 (1995)
Lei, M., Wang, Z.Z., Feng, Z.J.: Detecting nonlinearity of action surface EMG signal. Phys. Lett. A 290(5-6), 297–303 (2001)
Lei, M., Wang, Z.Z., Feng, Z.J.: A method of embedding dimension estimation based on symplectic geometry. Phys. Lett. A 303, 179–189 (2002)
Lei, M., Meng, G.: Symplectic principal component analysis: a new method for time series analysis. Math. Prob. in Eng. 2011, article ID 793429, 14 pages (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Min, L., Guang, M., Yudong, G., Kaili, Z. (2012). Distribution Rates Analysis of Symplectic Geometry Spectrum for Surface EMG Signals on Healthy Hand Muscles. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33509-9_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-33509-9_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33508-2
Online ISBN: 978-3-642-33509-9
eBook Packages: Computer ScienceComputer Science (R0)