Spectral Collaborative Representation based Classification for hand gestures recognition on electromyography signals

Biomedical Signal Processing and Control

Volumn 24, Issue , 2016, Pages 11-18

  Boyali, Ali ^a   Hashimoto, Naohisa ^a  

^a NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

Author keywords

99 00; MSC 00 01

Indexed keywords

AUTOMATION; CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; COMPUTER AIDED DIAGNOSIS; FEATURE EXTRACTION; FUZZY NEURAL NETWORKS; GESTURE RECOGNITION; HEURISTIC METHODS; KNOWLEDGE REPRESENTATION; PALMPRINT RECOGNITION; SAMPLING;

99-00; COLLABORATIVE REPRESENTATIONS; ELECTROMYOGRAPHY SIGNALS; HUMAN COMPUTER INTERFACES; MSC 00-01; REPRESENTATIVE SAMPLE; SUB-SPACE CLUSTERING; SURFACE ELECTROMYOGRAPHY SIGNALS;

BIOMEDICAL SIGNAL PROCESSING;

ACCURACY; ARTICLE; AUTOMATED PATTERN RECOGNITION; CLASSIFICATION ALGORITHM; CLUSTER ANALYSIS; ELECTROMYOGRAPHY; GESTURE; HAND MOVEMENT; HUMAN; PRIORITY JOURNAL; SIGNAL DETECTION; SPECTRAL COLLABORATIVE REPRESENTATION BASED CLASSIFICATION;

EID: 84941911534     PISSN: 17468094     EISSN: 17468108     Source Type: Journal    
DOI: 10.1016/j.bspc.2015.09.001     Document Type: Article

References (40)

1. J.P. Clarys, and J. Cabri Electromyography and the study of sports movements: a review J. Sports Sci. 11 5 1993 379 448
2. N. Masso, F. Rey, D. Romero, G. Gual, L. Costa, and A. German Surface electromyography applications in the sport Apunts Med. L'Esport 45 2010 127 136
3. G. Shan, and P. Visentin EMG Applications in Studies of Arts 2012 INTECH Open Access Publisher
4. J.P. Clarys Electromyography in sports and occupational settings: an update of its limits and possibilities Ergonomics 43 10 2000 1750 1762
5. S.N. Kale, and S.V. Dudul Intelligent noise removal from EMG signal using focused time-lagged recurrent neural network Appl. Comput. Intell. Soft Comput. 2009 2009 1
6. M. Hussain, M. Reaz, M.I. Ibrahimy, N. Mastorakis, V. Mladenov, Z. Bojkovic, D. Simian, S. Kartalopoulos, A. Varonides, C. Udriste, and et al. SEMG signal processing and analysis using wavelet transform and higher order statistics to characterize muscle force WSEAS International Conference. Proceedings. Mathematics and Computers in Science and Engineering, No. 12, WSEAS 2008
7. K. Yana, H. Marushima, H. Mine, and N. Takeuchi Bispectral analysis of filtered impulse processes with applications to the analysis of bioelectric phenomena Workshop on Higher-Order Spectral Analysis, 1989 IEEE 1989 140 145
8. A.O. Andrade, S. Nasuto, P. Kyberd, C.M. Sweeney-Reed, and F. Van Kanijn EMG signal filtering based on empirical mode decomposition Biomed. Signal Process. Control 1 1 2006 44 55
9. M. Taghizadeh-Sarabi, M.R. Daliri, and K.S. Niksirat Decoding objects of basic categories from electroencephalographic signals using wavelet transform and support vector machines Brain Topogr. 28 1 2014 33 46
10. R.H. Chowdhury, M.B. Reaz, M.A.B.M. Ali, A.A. Bakar, K. Chellappan, and T.G. Chang Surface electromyography signal processing and classification techniques Sensors 13 9 2013 12431 12466
11. P. Baldi Autoencoders, unsupervised learning, and deep architectures Unsupervised and Transfer Learning Challenges in Machine Learning 7 2012 43
12. L. Wei, H. Hu, and K. Yuan Use of forehead bio-signals for controlling an intelligent wheelchair IEEE International Conference on Robotics and Biomimetics, 2008. ROBIO 2008 2009 IEEE 108 113
13. A. Subasi, M. Yilmaz, and H.R. Ozcalik Classification of EMG signals using wavelet neural network J. Neurosci. Methods 156 1 2006 360 367
14. A. Del Boca, and D.C. Park Myoelectric signal recognition using fuzzy clustering and artificial neural networks in real time 1994 IEEE International Conference on Neural Networks, 1994. IEEE World Congress on Computational Intelligence, vol. 5 1994 IEEE 3098 3103
15. M. Khezri, and M. Jahed A neuro-fuzzy inference system for SEMG-based identification of hand motion commands IEEE Trans. Ind. Electron. 58 5 2011 1952 1960
16. G.R. Naik, D.K. Kumar, and M. Palaniswami Multi run ica and surface emg based signal processing system for recognising hand gestures 8th IEEE International Conference on Computer and Information Technology, 2008. CIT 2008 2008 IEEE 700 705
17. M.R. Ahsan, M.I. Ibrahimy, O.O. Khalifa, and et al. EMG signal classification for human computer interaction: a review Eur. J. Sci. Res. 33 3 2009 480 501
18. P. Konrad The ABC of EMG: A Practical Introduction to Kinesiological Electromyography, Version 1.0, Noraxon 2005
19. I. Rodríguez-Carreño, L. Gila-Useros, and A. Malanda-Trigueros Motor unit action potential duration: measurement and significance Adv. Clin. Neurophysiol. 2012 133
20. Thalmic Labs Myo User Guide 2015 https://developer.thalmic.com/docs/api-reference/platform/getting-started.html
21. L. Zhang, M. Yang, and X. Feng Sparse representation or collaborative representation: which helps face recognition? 2011 IEEE International Conference on Computer Vision (ICCV) 2011 IEEE 471 478
22. J. Wright, A.Y. Yang, A. Ganesh, S.S. Sastry, and Y. Ma Robust face recognition via sparse representation IEEE Trans. Pattern Anal. Mach. Intell. 31 2 2009 210 227
23. L. Zhang, M. Yang, X. Feng, Y. Ma, D. Zhang, Collaborative representation based classification for face recognition, arXiv:1204.2358.
24. D.G. Manolakis, V.K. Ingle, and S.M. Kogon Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering, and Array Processing vol. 46 2005 Artech House Norwood
25. Z. Zhang, and B.D. Rao Recovery of block sparse signals using the framework of block sparse Bayesian learning 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2012 IEEE 3345 3348
26. J. Huang, T. Zhang, and D. Metaxas Learning with structured sparsity J. Mach. Learn. Res. 12 2011 3371 3412
27. A. Boyali, and N. Hashimoto Block-sparse representation classification based gesture recognition approach for a robotic wheelchair Intelligent Vehicles Symposium Proceedings, 2014 IEEE 2014 IEEE 1133 1138
28. A. Boyali, N. Hashimoto, and O. Matsumoto A signal pattern recognition approach for mobile devices and its application to braking state classification on robotic mobility devices Robot. Auton. Syst. 2015 Elsevier
29. L. Harvill Applied Matrix Algebra 2011 Xlibris
30. H. Karner, J. Schneid, and C.W. Ueberhuber Spectral decomposition of real circulant matrices Linear Algebra Appl. 367 2003 301 311
31. D. Bini Toeplitz matrices, algorithms and applications ERCIM News 22 1995
32. J.F. Henriques, R. Caseiro, P. Martins, and J. Batista High-speed tracking with kernelized correlation filters IEEE Trans. Pattern Anal. Mach. Intell. 37 3 2015 583 596
33. H. Hassani Singular spectrum analysis: methodology and comparison J. Data Sci. 5 2007 239 257
34. Y. Saad Numerical Methods for Large Eigenvalue Problems vol. 158 1992 SIAM
35. A. Boyali, and M. Kavakli A robust gesture recognition algorithm based on sparse representation, random projections and compressed sensing 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA) 2012 IEEE 243 249
36. A. Boyali, N. Hashimoto, and O. Matsumato Hand posture control of a robotic wheelchair using a leap motion sensor and block sparse representation based classification SMART 2014, the Third International Conference on Smart Systems, Devices and Technologies 2014 20 25
37. E. Elhamifar, and R. Vidal Sparse subspace clustering: algorithm, theory, and applications IEEE Trans. Pattern Anal. Mach. Intell. 35 11 2013 2765 2781
38. G. Liu, Z. Lin, S. Yan, J. Sun, Y. Yu, and Y. Ma Robust recovery of subspace structures by low-rank representation IEEE Trans. Pattern Anal. Mach. Intell. 35 1 2013 171 184
39. S. Tierney, J. Gao, and Y. Guo Subspace clustering for sequential data Proc. Computer Vision and Pattern Recognition (CVPR) 2014 IEEE 1019 1026
40. A. Boyali, N. Hashimoto, O. Matsumoto, Paradigm shift in continuous signal pattern classification: mobile ride assistance system for two-wheeled mobility robots, arXiv:1506.04810.