Application of linear discriminant analysis in dimensionality reduction for hand motion classification

Measurement Science Review

Volumn 12, Issue 3, 2012, Pages 82-89

  Phinyomark, A ^a   Hu, H ^b   Phukpattaranont, P ^a   Limsakul, C ^a  

^a PRINCE OF SONGKLA UNIVERSITY   (Thailand)

^b UNIVERSITY OF ESSEX   (United Kingdom)

Author keywords

Electromyography signal; EMG; feature extraction; feature projection; kernel discriminant analysis; orthogonal fuzzy neighborhood discriminant analysis; orthogonal LDA; QR decomposition; uncorrelated LDA

Indexed keywords

ELECTROMYOGRAPHY SIGNALS; EMG; FEATURE PROJECTION; KERNEL DISCRIMINANT ANALYSIS; ORTHOGONAL LDA; Q R DECOMPOSITION; UNCORRELATED LDA;

DISCRIMINANT ANALYSIS; ELECTROMYOGRAPHY; FACE RECOGNITION; FEATURE EXTRACTION; IMAGE RETRIEVAL; TIME DOMAIN ANALYSIS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84861683668     PISSN: 13358871     EISSN: None     Source Type: Journal    
DOI: 10.2478/v10048-012-0015-8     Document Type: Review

References (47)

1. Merletti, R., Hermens, H. (2004). Detection and conditioning of the surface EMG signal. In Merletti R., Parker, P. (eds.) Electromyography: Physiology, Engineering, and Noninvasive Applications. New Jersey: John Wiley & Sons, 107-132.
2. Oskoei, M.A., Hu, H. (2007). Myoelectric control systems-A survey. Biomed. Signal Process. Control, 2 (4), 275-294. (Pubitemid 47610329)
3. Parker, P.A., Englehart, K.B., Hudgins, B.S. (2004). Control of powered upper limb prostheses. In Merletti, R., Parker, P. (eds.) Electromyography: Physiology, Engineering, and Noninvasive Applications. New Jersey: John Wiley & Sons, 453-476.
4. Phinyomark, A., Phukpattaranont, P., Limsakul, C. (2011). A review of control methods for electric power wheelchairs based on electromyography (EMG) signals with special emphasis on pattern recognition. IETE Technical Review, 28 (4), 316-326.
5. Jacobson, S.C., Knutti, D.F., Johnson, R.T., Shears, H.H. (1982). Development of the Utah artificial arm. IEEE Trans. Biomed. Eng., 29 (4), 249-269. (Pubitemid 12130890)
6. Hudgins, B., Parker, P., Scott, R.N. (1993). A new strategy for multifunction myoelectric control. IEEE Trans. Biomed. Eng., 40 (1), 82-94. (Pubitemid 24060692)
7. Light, C.M., Chappell, P.H. (2000). Development of a lightweight and adaptable multiple-axis hand prosthesis. Med. Eng. Phys., 22 (10), 679-684. (Pubitemid 32378729)
8. Englehart, K., Hudgin, B., Parker, P.A., Stevenson, M. (1999). Classification of the myoelectric signal using time-frequency based representations. Med. Eng. Phys., 21 (6-7), 431-438. (Pubitemid 29537720)
9. Khezri, M., Jahed, M. (2009). An exploratory study to design a novel hand movement identification system. Comput. Biol. Med., 39 (5), 433-442.
10. Artemiadis, P.K., Kyriakopoulos, K.J. (2010). EMGbased control of a robot arm using low-dimensional embeddings. IEEE Trans. Robotics, 26 (2), 393-398.
11. Rivera-Alvidrez, Z., Kalmar, R.S., Ryu, S.I., Shenoy, K.V. (2010). Low-dimensional neural features predict muscle EMG signals. In Annual Internal Conference of the IEEE Engineering in Medicine and Biology Society, 31 Aug. - 4 Sept. 2010. IEEE, 6027-6033.
12. Khushaba, R.N., Al-Jumaily, A., Al-Ani, A. (2007). Novel feature extraction method based on fuzzy entropy and wavelet packet transform for myoelectric control. In ISCIT '07: International Symposium on Communications and Information Technologies, 17-19 October 2007. IEEE, 352-357.
13. Chu, J.-U., Moon, I., Mun, M.-S. (2006). A real-time EMG pattern recognition system based on linearnonlinear feature projection for a multifunction myoelectric hand. IEEE Trans. Biomed. Eng., 53 (11), 2232-2239. (Pubitemid 44629861)
14. Englehart, K., Hudgin, B., Parker, P.A. (2001). A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Trans. Biomed. Eng., 48 (3), 302-311. (Pubitemid 32249578)
15. Chu, J.-U., Moon, I., Lee, Y.-J., Kim, S.-K., Mun, M.-S. (2007). A supervised feature-projection-based real-time EMG pattern recognition for multifunction myoelectric hand control. IEEE-ASME Trans. Mechatron., 12 (3), 282-290. (Pubitemid 47018779)
16. Chan, A.D.C., Green, G.C. (2007). Myoelectric control development toolbox. In 30th Conference of the Canadian Medical & Biological Engineering Society, 16-19 June 2007. CMBES, M0100.
17. Khushaba, R.N., Al-Ani, A., Al-Jumaily, A. (2010). Orthogonal fuzzy neighborhood discriminant analysis for multifunction myoelectric hand control. IEEE Trans. Biomed. Eng., 57 (6), 1410-1419.
18. Liu, Y.H., Huang, H.P., Weng, C.H. (2007). Recognition of electromyographic signals using cascaded kernel learning machine. IEEE-ASME Trans. Mechatron., 12 (3), 253-264.
19. Khushaba, R.N., Al-Jumaily, A., Al-Ani, A. (2009). Evolutionary fuzzy discriminant analysis feature projection technique in myoelectric control. Pattern Recognition Lett., 30 (7), 699-707.
20. Khezri, M., Jahed, M. (2011). A neuro-fuzzy inference system for sEMG-based identification of hand motion commands. IEEE Trans. Ind. Electron., 58 (5), 1952-1960.
21. Rafiee, J., Rafiee, M.A., Yavari, F., Schoen, M.P. (2011). Feature extraction of forearm EMG signals for prosthetics. Expert Syst. Appl., 38 (4), 4058-4067.
22. Phinyomark, A., Limsakul, C., Phukpattaranont, P. (2011). Application of wavelet analysis in EMG feature extraction for pattern classification. Meas. Sci. Rev., 11 (2), 45-52.
23. Phinyomark, A., Nuidod, A., Phukpattaranont, P. Limsakul, C. (2012). Feature extraction and reduction of wavelet transform coefficients for EMG pattern classification. Electronics and Electrical Engineering, 122 (6), in press.
24. Fukunaka, K. (1990). Introduction to Statistical Pattern Recognition. Academic Press.
25. Schafer, K.C., Balog, J., Szaniszlo, T., Szalay, D., Mezey, G., Denes, J., Bognar, L., Oertel, M., Takats, Z. (2011). Real time analysis of brain tissue by direct combination of ultrasonic surgical aspiration and sonic spray mass spectrometry. Analytical Chemistry, 83 (20) 7729-7735.
26. Belhumeour, P.N., Hespanha, J.P., Kriegman, D.J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. Patt. Anal. Mach. Int., 19 (7), 711-720. (Pubitemid 127762263)
27. Sakai, M., Kitaoka, N., Takeda, K. (2010). Acoustic feature transformation based on discriminant analysis preserving local structure for speech recognition. IEICE Trans. Inform. Syst., E93D (5), 1244-1252.
28. Howland, P., Jeon, M., Park, H. (2003). Structure preserving dimension reduction for clustered text data based on the generalized singular value decomposition. SIAM J. Matrix Anal. Applicat., 25 (1), 165-179. (Pubitemid 38260434)
29. Jin, Z., Yang, J.Y., Tang, Z.M., Hu, Z.S. (2001). A theorem on the uncorrelated optimal discriminant vectors. Patt. Recog., 34 (10), 2041-2047. (Pubitemid 32760633)
30. Ye, J. (2005). Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. Journal of Machine Learning Research, 6, 483-502.
31. Ye, J., Li, Q. (2004). LDA/QR: An efficient and effective dimension reduction algorithm and its theoretical foundation. Patt. Recog., 37 (4), 851-854. (Pubitemid 38301060)
32. Krzanowski, W.J., Jonathan, P., McCarthy, W.V., Thomas, M.R. (1995). Discriminant analysis with singular covariance matrices: Methods and applications to spectroscopic data. J. Roy. Statist. Soc. Ser. C-Appl. Stat., 44 (1), 101-115.
33. Baudat, G., Anouar, F. (2000). Generalized discriminant analysis using a kernel approach. Neural Comput., 12 (10), 2385-2404.
34. Tao, X., Ye, J., Li, Q., Janardan, R., Cherkassky, V. (2004). Efficient Kernel discriminant analysis via QR decomposition. In NIPS 2004: 18th Annual Conference on Neural Information Processing Systems, 13-18 December 2004. NIPS, 1529-1536.
35. Phinyomark, A., Phukpattaranont, P., Limsakul, C., Phothisonothai, M. (2011). Electromyography (EMG) signal classification based on detrended fluctuation analysis. Fluctuation and Noise Lett., 10 (3), 281-301.
36. Oskoei, M.A., Hu, H. (2008). Support vector machinebased classification scheme for myoelectric control applied to upper limb. IEEE Trans. Biomed. Eng., 55 (8), 1956-1965. (Pubitemid 352013532)
37. Kaufmann, P., Englehart, K., Platzner, M. (2010). Fluctuating EMG signals: Investigating long-term effects of pattern matching algorithms. In Annual Internal Conference of the IEEE Engineering in Medicine and Biology Society, 31 Aug. - 4 Sept. 2010. IEEE, 6357-6360.
38. Geethanjali, P., Ray, K.K. (2011). Identification of motion from multi-channel EMG signals for control of prosthesis hand. Australas. Phys. Eng. Sci. Med., 34 (3), 419-427.
39. Du, Y.-C., Lin, C.-H., Shyu, L.-Y., Chen, T. (2010). Portable hand motion classifier for multi-channel surface electromyography recognition using grey relational analysis. Expert Syst. Appl., 37 (6), 4283-4291.
40. Khushaba, R.N., Al-Ani, A., Al-Jumaily, A. (2010). Orthogonal fuzzy neighborhood discriminant analysis for multifunction myoelectric hand control. IEEE Trans. Biomed. Eng., 57 (6), 1410-1419.
41. Yan, Z., Wang, Z., Xie, H. (2008). Joint application of rough set-based feature reduction and fuzzy LS-SVM classifier in motion classification. Med. Biol. Eng. Comput., 46 (6), 519-527.
42. Yan, Z., Wang, Z., Xie, H. (2008). The application of mutual information-based feature selection and fuzzy LS-SVM-based classifier in motion classification. Comput. Method. Program. Biomed., 90 (3), 275-284.
43. Clancy, E.A., Morin, E.L., Merletti, R. (2002). Sampling, noise-reduction and amplitude estimation issues in surface electromyography. J. Electromyograph. Kinesiol., 12 (1), 1-16. (Pubitemid 34132825)
44. Naik, G.R., Kumar, D.K., Arjunan, S.P. (2010). Pattern classification of Myoelectrical signal during different Maximum Voluntary Contractions: A study using BSS techniques. Meas. Sci. Rev., 10 (1), 1-6.
45. Boostani, R., Moradi, M.H. (2003). Evaluation of the forearm EMG signal features for the control of a prosthetic hand. Physiol. Meas., 24 (2), 309-319. (Pubitemid 36648398)
46. Phinyomark, A., Phukpattaranont, P., Limsakul, C. (2011). Wavelet-based denoising algorithm for robust EMG pattern recognition. Fluctuation and Noise Lett., 10 (2), 157-167.
47. Hussain, M.S., Mamun, Md. (2012). Effectiveness of the wavelet transform on the surface EMG to understand the muscle fatigue during walk. Meas. Sci. Rev., 12 (1), 28-33.