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Improvements of Multi-features Extraction for EMG for Estimating Wrist Movements

근전도 신호기반 손목 움직임의 추정을 위한 다중 특징점 추출 기법 알고리즘

  • Received : 2012.02.09
  • Accepted : 2012.04.23
  • Published : 2012.05.01

Abstract

In this paper, the multi feature extraction algorithm for estimation of wrist movements based on Electromyogram(EMG) is proposed. For the extraction of precise features from the EMG signals, the difference absolute mean value(DAMV), the mean absolute value(MAV), the root mean square(RMS) and the difference absolute standard deviation value(DASDV) to consider amplitude characteristic of EMG signals are used. We figure out a more accurate feature-set by combination of two features out of these, because of multi feature extraction algorithm is more precise than single feature method. Also, for the motion classification based on EMG, the linear discriminant analysis(LDA), the quadratic discriminant analysis(QDA) and k-nearest neighbor(k-NN) are used. We implemented a test targeting twenty adult male to identify the accuracy of EMG pattern classification of wrist movements such as up, down, right, left and rest. As a result of our study, the LDA, QDA and k-NN classification method using feature-set with MAV and DASDV showed respectively 87.59%, 89.06%, 91.75% accuracy.

Acknowledgement

Supported by : 정보통신산업진흥원

References

  1. M. Ferdjallah, J. J. Wertsch, and G. F. Hams, "Effects of Surface Electrode Size on Computer Simulated Surface Motor Unit Potentials," Electromyography and Clinical Neurophysiology, vol. 39, pp. 259-265, 1990.
  2. C. Jensen, O. Vasseljen, R. H. Westgaard, "The influence of Electrode Position on Bipolar Surface Electromyogram Recordings of the Upper Trapezius Muscle," Eur. J. Appl Physiol., vol. 67, pp. 266-273, 1993. https://doi.org/10.1007/BF00864227
  3. S. Thusneyapan, G. I. Zahalak "A Practical Electrode-Array Myoprocessor for Surface Electromyography," IEEE Trans. Biomed Eng., vol. 36, no. 2. February. 1989.
  4. 엄현우, 최한순,남윤수, "EMG 센서를 이용한 재활 목적을 지닌 보행 보조 기구의 실시간 제어," 한국정밀공학회논문지, 추계학술대회 논문집, pp. 49-50, October, 2009.
  5. Adaptive fuzzy k-NN classifier for EMG signal decomposition vol 28, no 7, pp. 694-709, September, 2006. https://doi.org/10.1016/j.medengphy.2005.11.001
  6. K.S Kim, H.H Choi, C.S Moon, C.W Mun, "Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions," Current Applied Physics, vol 11, no 3, pp 740-745, May 2011. https://doi.org/10.1016/j.cap.2010.11.051
  7. Wei-Shi Zhenga,c, J.H. Laib,c, Stan Z, "1D-LDA vs. 2D-LDA:When is vector-based linear discriminant analysis better than matrix-based?," Pattern Recognition, vol. 41, no. 7, pp. 2156-2172, 2008. https://doi.org/10.1016/j.patcog.2007.11.025
  8. Alok S, Kuldip K. Paliwala, Godfrey C. Onwubolub, "Class-dependent PCA,MDCand LDA:Acombined classifier for pattern classification," Pattern Recognition, vol. 39, no. 7, pp.1215-1229, July 2006. https://doi.org/10.1016/j.patcog.2006.02.001
  9. R. M. Balabin, R. Z. Safieva, E. I. Lomakina, "Gasoline classification using near infrared (NIR) spectroscopy data Comparison of multivariate techniques," Analytica Chimica Acta, vol. 671, no. 1-2, 25, pp. 27-35, June 2010. https://doi.org/10.1016/j.aca.2010.05.013
  10. D. Michie, D.J. Spiegelhalter, C.C. Taylor, "Mach. Learn, Neural and Statistical Classification," February 17, 1994.
  11. 한학용, 패턴인식 개론, 한빛미디어(주), pp. 163-539, 2005.
  12. BV. Dasarathy, Nearest Neighbor (NN) Norms: NN Pattern Classification, Ieee Computer Society, December 1990.
  13. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed., Academic Press, Boston, October 12, 1990.
  14. N. Cristianini, J. Shave-Taylor, An Introduction to Support Vector Machine (and Other Kernel-Based Learning Methods), Cambridge Univ. Press, Cambridge, UK, 2000.
  15. M. Pontil, A. Verri, "Properties of support vector machines, Neural Comput," Neural Computation, vol. 10, no. 4, pp. 955-974, May, 1998. https://doi.org/10.1162/089976698300017575
  16. 송영록, 김서준, 정의철, 이상민, "Gaussian Mixture Model 기반 전완 근전도 패턴 분류 알고리즘," 재활복지공학회논문지, 5권 1호, pp. 95-101, 2011.
  17. Cherchi. E, Guevara C.A, "A Monte Carlo experiment to analyze the curse of dimensionality in estimating random coefficients models with a full variance-covariance matrix," Transportation Research, 2011.

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