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| Electromyography Analysis for Person Identification
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Full
text: |
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Source |
International Journal of Biometrics and Bioinformatics (IJBB) |
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Table of Contents |
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Download
Complete Issue PDF(2.29MB) |
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Volume: 5 Issue: 3 |
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Pages: 149-201 |
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Publication
Date: July / August 2011 |
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ISSN
(Online): 1985-2347 |
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Pages |
172 - 179 |
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Author(s) |
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Published
Date |
05-08-2011 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: Biometrics, Electromyogram, Gaussian Mixture Model (GMM), Identification, Vector Quantization |
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| Physiological descriptions of the electromyography signal and other literature say that when we make a motion, the motor neurons of respective muscle get activated and all the innervated motor units in that zone produce motor unit action potential. These motor unit action potentials travel through the muscle fibers with conduction velocity and superimposed signal gets recorded at the electrode site. Here we have taken an analogy from the speech production system model as the excitation signal travels through vocal tract to produce speech; similarly, an impulse train of firing rate frequency goes through the system with impulse response of motor unit action potentials and travels along the muscle fiber of that person. As the vocal tract contains the speaker information, we can also separate the muscle fiber pattern part and motor unit discharge pattern through proper selection of features and its classification to identify the respective person. Cepstral and non uniform filter bank features models the variation in the spectrum of the signals. Vector quantization and Gaussian mixture model are the two techniques of pattern matching have been applied. |
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| 1 |
C. N. Christakos, "A population stochastic model of skeletal muscle and its use in the study of frequency characteristics of the muscle output activity with particular reference to tremor," Ph.D. dissertation, Chelsea Univ. College, London, England, 1980.
|
|
|
| 2 |
P.Lago and N.Jones, “Effect of motor unit firing statistics on EMG spectra” Med. Biol Eng. Comput., Vol.15, pp. 648-655, 1977.
|
|
|
| 3 |
L. Lindstrom and R. Magnusson, "Interpretation of myoelectric power spectra. A model and its applications” Proc. IEEE, vol. 64, pp. 653-662, 1977.
|
|
|
| 4 |
G. Agarwal and G. Gottlieb "An analysis of the electromyogram by Fourier simulation and experimental results” IEEE Trans Biomed. Eng., vol. BME-22, pp. 225-229, May 1975.
|
|
|
| 5 |
R. Le Fever and C. De Luca, "The contribution of individual motor units to the EMG power spectrum” in Proc. 29th Annu. Conf Eng., Med. Biol., Vol. 18, 1976, p.91.
|
|
|
| 6 |
G. Inbar, J. Allin, E. Golos, W. Koehler, and H. Kranz,"EMG spectral shift with muscle length, tension, and fatigue" in Proc. IEEE Melecon Conf., 1981.
|
|
|
| 7 |
N. S. Yogendra, and G. Phalguni, “ECG to Individual Identification”,IEEE Second International Conf on Biometrics: Theory, Applications and Systems (BTAS 2008), Washington DC, USA.
|
|
|
| 8 |
W. Yongjin, A. Foteini, H. Dimitrios, and N. P Konstatantinos,“Analysis of Human Electrocardiogram for Biometric Recognaition,”EURASIP Journal of advance in signal processing, 2008.doi:10.1155/2008/148658.
|
|
|
| 9 |
D. C. Chan, M. M Hamdy, A. Badre and V. Badee, “Person Identification using Electrocardiograms” CCECE apos;06. Canadian Conference on May 2006 pp:1 – 4.Y. Wang, K. N. Plataniotis, and D. Hatzinakos, “Integrating analytic and appearance attributes for human identification from ECG signal,”Proc of Biometrics symposiums, September 2006.
|
|
|
| 10 |
T. W. Shen, W. J. Tompkins, Y. H. Hu, “One-lead ECG for Identity Verification, Proc IEEE EMBS/BMES conf,62-63,2002.J. M. Irvine, K. B. Wiederhold, L. W. Gavshon, S. Israel, S. B. McGehee, R. Meyer, M. D. Wiederhold, “Heart rate variability: A new biometric for human identification”, Proc International Conference on Artificial Intellegent, 1106-1111,2001.
|
|
|
| 11 |
D. Martin, D. Lodrova, “Liveness detection for biometric systems based on papillary lines,” Int. J. Security and Its Applications, vol. 2, no. 4, Oct. 2008.
|
|
|
| 12 |
F. Su, L.-W. Xia, A. Cai, J.-S. Ma, Y.-B. Wu, “EEG-based personal identification: from proof-of-concept to a practical system,” Proc. Int. Conf. Pattern Recognition, Istanbul, Turkey, Aug., 2010.
|
|
|
| 13 |
C. R. Hema, M. P. Paulraj, K. Harkirenjit, “Brain signatures: a modality for biometric authentication,” Int. Conf. Electronic Design, 2008, pp. 1– 4.
|
|
|
| 14 |
N. Markus, W. Marc, S. Johannes, “Test–retest reliability of resting EEG spectra validates a statistical signature of persons,” Clinical Neurophysiology, vol. 118, 2007, pp. 2519–2524.
|
|
|
| 15 |
M. Chisei, B. Sadanao, N. Isao, “Biometric person authentication using new spectral features of electroencephalogram (EEG),” Int. Symp. Intelligent Signal Processing and Communication Systems, 2008.
|
|
|
| 16 |
S. Sun, “Multitask learning for EEG-based biometrics,” Int. Conf. Pattern Recognition, 2008, pp. 1–4.H.
|
|
|
| 17 |
Chen, X.-D. Lv, Z. J. Wang, “Hashing the mAR coefficients from EEG data for person authentication,” IEEE Int. Conf. Acoustics, Speech and Signal Processing, 2009, pp. 1445–1448.
|
|
|
| 18 |
A. Yazdani, A. Roodaki, S. H. Rezatofighi, K. Misaghian, S. K. Setarehdan, “Fisher linear discriminant based person identification using visual evoked potentials,” IEEE Int. Conf. Signal Processing, 2008, pp. 1677–1680.
|
|
|
| 19 |
Recording techniques. In Selected Topics in Surface Electromyography for Use in the Occupational Setting: Expert Perspective. U.S. Department of Health and Human Services. DHHS (NIOSH) Publication No 91-100.
|
|
|
| 20 |
E.W.Theado, G.G.Knapik and W.S.Marras, “Modification of an EMG –assisted biomechanical model for pulling and pushing” in International Journal of Industrial Ergonomics, 37(2007)825-831.
|
|
|
| 21 |
David G.Loyd, Thor F.Beiser,” An EMG driven Musculoskeletal Model to estimate muscle forces and Knee joints moments in vivo” Journal of Biomechanics 36(2003),765-776.
|
|
|
| 22 |
A. P. Dempster, N. M Laird, and D. B. Rubin, “Maximum Likelihood from Incomplete Data via the EM algorithm,” J. Roy. Statist. Soc. B, vol. 39, pp. 1-38, 1977.
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| Suresh.M : Colleagues
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| Krishnamohan.P.G : Colleagues
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| Mallikarjun S Holi : Colleagues
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