臺灣博碩士論文加值系統

The myoelectric signal is one of the bio-signals which is used by humans to control equipment. To achieve this purpose, a good myoelectric pattern recognition (MPR) is required. The applied classifier and extracted feature set greatly affect the success of M-PR. This paper proposes a hybrid and fast classifier, extreme learning machine (ELM) optimized by improved hybrid particle swarm optimization with wavelet mutation (improved swarm-wavelet). ELM is an improvement of neural network that keeps off repetitive learning to save the training time. In addition to improve the performance of MPR, this paper evaluates the optimization of ELM using improved swarm-wavelet. The swarm-wavelet is improved by using the particle refresh and applying velocity improvement to avoid trapping in local minima. The superiority of the improved swarm-wavelet has been shown in facing up a set of benchmark functions. In ELM, the improved swarm-wavelet is used to find the most suitable parameters to increase the classification accuracy. Furthermore, this paper provides comparisons of improved swarm-wavelet-ELM, swarm-wavelet-ELM and standard swarm-ELM. The experimental results show that the improved swarm-wavelet-ELM is the most accurate classifier with the mean accuracy of 99.6%.

The myoelectric signal is one of the bio-signals which is used by humans to control equipment. To achieve this purpose, a good myoelectric pattern recognition (MPR) is required. The applied classifier and extracted feature set greatly affect the success of M-PR. This paper proposes a hybrid and fast classifier, extreme learning machine (ELM) optimized by improved hybrid particle swarm optimization with wavelet mutation (improved swarm-wavelet). ELM is an improvement of neural network that keeps off repetitive learning to save the training time. In addition to improve the performance of MPR, this paper evaluates the optimization of ELM using improved swarm-wavelet. The swarm-wavelet is improved by using the particle refresh and applying velocity improvement to avoid trapping in local minima. The superiority of the improved swarm-wavelet has been shown in facing up a set of benchmark functions. In ELM, the improved swarm-wavelet is used to find the most suitable parameters to increase the classification accuracy. Furthermore, this paper provides comparisons of improved swarm-wavelet-ELM, swarm-wavelet-ELM and standard swarm-ELM. The experimental results show that the improved swarm-wavelet-ELM is the most accurate classifier with the mean accuracy of 99.6%.

Contents
Abstract
Acknowledgements
Contents
List of Tables
List of Figures
1 Introduction
1.1 Research Background
1.2 Outline Report
2 Literature Review
2.1 Extreme Learning Machine (ELM)
2.1.1 The concept of ELM
2.1.2 ELM with Kernel
2.2 Particle Swarm Optimization (PSO)
2.2.1 Hybrid PSO with Wavelet Mutation
2.2.2 Particle Refresh and Velocity Improvement of PSO
3 Methodology
3.1 Benchmark Test Functions
3.2 Proposed System
3.3 Data Acquisition and Processing
3.3.1 Subjects of experiments
3.3.2 Signal acquisition device
3.3.3 Acquisition protocol
3.3.4 Data segmentation
3.3.5 Feature extraction
3.3.6 Classification
4 Experimental Results
4.1 Benchmark Test Functions: Results and Analysis
4.1.1 Experimental Setup
4.1.2 Results and Analysis
4.1.3 The t-Test
4.2 Myoelectric Pattern Recognition
4.2.1 Number of Channels
4.2.2 Window Length
4.2.3 Final Result and Convergence Rate
5 Conclusion
References
Appendix A：Benchmark Functions

[1] TouchBionics, “The big picture: Bionic hand,” In Spectrum, vol. 44, no. 22, 2007.
[2] E. Scheme and K. Englehart, “Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use,” Journal of rehabilitation research and development, vol. 48, no. 6, p. 643, 2011.
[3] N. Uchida, A. Hiraiwa, N. Sonehara, and K. Shimohara, “Emg pattern recognition by neural networks for multi fingers control,” in Engineering in Medicine and Biology Society, 1992 14th Annual International Conference of the IEEE, vol. 3, pp. 1016–1018, IEEE, 1992.
[4] G. Tsenov, A. Zeghbib, F. Palis, N. Shoylev, and V. Mladenov, “Neural networks for online classification of hand and finger movements using surface emg signals,” in Neural Network Applications in Electrical Engineering, 2006. NEUREL 2006. 8th Seminar on, pp. 167–171, IEEE, 2006.
[5] F. V. Tenore, A. Ramos, A. Fahmy, S. Acharya, R. Etienne-Cummings, and N. V. Thakor, “Decoding of individuated finger movements using surface electromyography,” IEEE transactions on biomedical engineering, vol. 56, no. 5, pp. 1427–1434, 2009.
[6] M. A. Oskoei and H. Hu, “Support vector machine-based classification scheme for myoelectric control applied to upper limb,” IEEE transactions on biomedical engineering, vol. 55, no. 8, pp. 1956–1965, 2008.
[7] S. W. Lee, K. M. Wilson, B. A. Lock, and D. G. Kamper, “Subject-specific myoelectric pattern classification of functional hand movements for stroke survivors,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 19, no. 5, pp. 558–566, 2011.
[8] S. Micera, J. Carpaneto, and S. Raspopovic, “Control of hand prostheses using peripheral information,” IEEE Reviews in Biomedical Engineering, vol. 3, pp. 48–68, 2010.
[9] J. Cao, Z. Lin, and G.-B. Huang, “Self-adaptive evolutionary extreme learning machine,” Neural processing letters, pp. 1–21, 2012.
[10] Y. Xu and Y. Shu, “Evolutionary extreme learning machine–based on particle swarm optimization,” Advances in Neural Networks-ISNN 2006, pp. 644–652, 2006.
[11] S.-H. Ling, H. H. Iu, K. Y. Chan, H.-K. Lam, B. C. Yeung, and F. H. Leung, “Hybrid particle swarm optimization with wavelet mutation and its industrial applications,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 38, no. 3, pp. 743–763, 2008.
[12] K. Kawakami and Z. Meng, “Improvement of particle swarm optimization,” PIERS Online, vol. 5, no. 3, pp. 261–264, 2009.
[13] Y. Wang and J. Liu, “Velocity improvement-particle swarm optimization,” in Foundations of Intelligent Systems, pp. 1133–1142, Springer, 2014.
[14] G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42, no. 2, pp. 513–529, 2012.
[15] Q.-Y. Zhu, A. K. Qin, P. N. Suganthan, and G.-B. Huang, “Evolutionary extreme learning machine,” Pattern recognition, vol. 38, no. 10, pp. 1759–1763, 2005.
[16] D. Serre, Matrices: Theory and applications. Graduate texts in mathematics, Springer, 2002.
[17] C. R. Rao and S. K. Mitra, “Generalized inverse of matrices and its applications,” 1971.
[18] G.-B. Huang, “An insight into extreme learning machines: random neurons, random features and kernels,” Cognitive Computation, vol. 6, no. 3, pp. 376–390, 2014.
[19] J. Kennedy and R. Eberhart, “Particle swarm optimization,” vol. IV, pp. 1942–1948, 1995.
[20] Z. Michalewicz, Genetic algorithms + data structures = evolution programs, 3rd ed. Springer, 1996.
[21] X. Yao, Y. Liu, and G. Lin, “Evolutionary programming made faster,” IEEE Transactions on Evolutionary computation, vol. 3, no. 2, pp. 82–102, 1999.
[22] D. H. Wolpert, W. G. Macready, et al., “No free lunch theorems for search,” tech. rep., Technical Report SFI-TR-95-02-010, Santa Fe Institute, 1995.
[23] D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE transactions on evolutionary computation, vol. 1, no. 1, pp. 67–82, 1997.
[24] K. Englehart and B. Hudgins, “A robust, real-time control scheme for multifunction myoelectric control,” IEEE transactions on biomedical engineering, vol. 50, no. 7, pp. 848–854, 2003.
[25] B. Hudgins, P. Parker, and R. N. Scott, “A new strategy for multifunction myoelectric control,” IEEE Transactions on Biomedical Engineering, vol. 40, no. 1, pp. 82–94, 1993.
[26] K. Englehart, B. Hudgin, and P. A. Parker, “A wavelet-based continuous classification scheme for multifunction myoelectric control,” IEEE Transactions on Biomedical Engineering, vol. 48, no. 3, pp. 302–311, 2001.
[27] T. R. Farrell and R. F. Weir, “The optimal controller delay for myoelectric prostheses,” IEEE Transactions on neural systems and rehabilitation engineering, vol. 15, no. 1, pp. 111–118, 2007.
[28] M. Zecca, S. Micera, M. C. Carrozza, and P. Dario, “Control of multifunctional prosthetic hands by processing the electromyographic signal,” Critical Reviews™ in Biomedical Engineering, vol. 30, no. 4-6, 2002.
[29] M. A. Oskoei and H. Hu, “Myoelectric control systems—a survey,” Biomedical Signal Processing and Control, vol. 2, no. 4, pp. 275–294, 2007.
[30] M. Zardoshti-Kermani, B. C. Wheeler, K. Badie, and R. M. Hashemi, “Emg feature evaluation for movement control of upper extremity prostheses,” IEEE Transactions on Rehabilitation Engineering, vol. 3, no. 4, pp. 324–333, 1995.
[31] K. Englehart, B. Hudgins, P. A. Parker, and M. Stevenson, “Classification of the myoelectric signal using time-frequency based representations,” Medical engineering & physics, vol. 21, no. 6, pp. 431–438, 1999.
[32] S.-H. Park and S.-P. Lee, “Emg pattern recognition based on artificial intelligence techniques,” IEEE transactions on Rehabilitation Engineering, vol. 6, no. 4, pp. 400–405, 1998.
[33] D. Farina and R. Merletti, “Comparison of algorithms for estimation of emg variables during voluntary isometric contractions,” Journal of Electromyography and Kinesiology, vol. 10, no. 5, pp. 337–349, 2000.
[34] A. Phinyomark, C. Limsakul, and P. Phukpattaranont, “A novel feature extraction for robust emg pattern recognition,” arXiv preprint arXiv:0912.3973, 2009.
[35] K. A. Farry, I. D. Walker, and R. G. Baraniuk, “Myoelectric teleoperation of a complex robotic hand,” IEEE Transactions on Robotics and Automation, vol. 12, no. 5, pp. 775–788, 1996.
[36] D. Tkach, H. Huang, and T. A. Kuiken, “Study of stability of time-domain features for electromyographic pattern recognition,” Journal of neuroengineering and rehabilitation, vol. 7, no. 1, p. 21, 2010.
[37] N. Mo, Z. Zou, K. Chan, and T. Pong, “Transient stability constrained optimal power flow using particle swarm optimisation,” IET generation, transmission & distribution, vol. 1, no. 3, pp. 476–483, 2007.
[38] L. H. Smith, L. J. Hargrove, B. A. Lock, and T. A. Kuiken, “Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification error and controller delay,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 19, no. 2, pp. 186–192, 2011.
[39] Y. Huang, K. B. Englehart, B. Hudgins, and A. D. Chan, “A gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses,” IEEE Transactions on Biomedical Engineering, vol. 52, no. 11, pp. 1801–1811, 2005.