Performance Comparison of Artificial Neural Network and Gaussian Mixture Model in Classifying Hand Motions by Using sEMG Signals

In this study, a home-made four channel sEMG amplifier circuit was designed for measu-ring of sEMG signals. The measured sEMG signals were recorded on to a computer with help of a DAQ board. The recorded sEMG signals were filtered first with a high-pass filter and afterwards a wavelet based filtering was applied to remove unwanted noises. Before applying of the wavelet based filtering, it was first determined which wavelet type, threshold selection rule and threshold would be suitable for the denoising process. As a second step, the recorded and denoised signals’ features were extracted. For classification of motions 8 time domain and 2 frequency domain features were used individually and in combinations. Lastly, seven different motions were classified and their classification performances were compared. In this study, classification rates of ANN and GMM classifiers were compared as regards features.

[1]  Mohammad Hassan Moradi,et al.  Evaluation of the forearm EMG signal features for the control of a prosthetic hand. , 2003, Physiological measurement.

[2]  J. Pauk.  Different techniques for EMG signal processing , 2008 .

[3]  Mehran Jahed,et al.  Real-time intelligent pattern recognition algorithm for surface EMG signals , 2007, Biomedical engineering online.

[4]  A. Phinyomark,et al.  An optimal wavelet function based on wavelet denoising for multifunction myoelectric control , 2009, 2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[5]  Toshio Fukuda,et al.  Neuro-fuzzy control of a robotic exoskeleton with EMG signals , 2004, IEEE Transactions on Fuzzy Systems.

[6]  Michael I. Jordan,et al.  On Convergence Properties of the EM Algorithm for Gaussian Mixtures , 1996, Neural Computation.

[7]  J. Pauk.  419. Different techniques for EMG signal processing , 2008 .

[8]  Marie-Françoise Lucas,et al.  Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization , 2008, Biomed. Signal Process. Control..

[9]  Peter J. Kyberd,et al.  EMG signal filtering based on Empirical Mode Decomposition , 2006, Biomed. Signal Process. Control..

[10]  C. Grimbergen,et al.  Investigation into the origin of the noise of surface electrodes , 2002, Medical and Biological Engineering and Computing.

[11]  M. Osman Tokhi,et al.  A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis , 2003, IEEE Transactions on Biomedical Engineering.

[12]  Ching-Fen Jiang,et al.  A Comparative Study of Wavelet Denoising of Surface Electromyographic Signals , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  N.V. Thakor,et al.  Towards the Control of Individual Fingers of a Prosthetic Hand Using Surface EMG Signals , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Pornchai Phukpattaranont,et al.  A Novel Feature Extraction for Robust EMG Pattern Recognition , 2009, ArXiv.

[15]  Kevin B. Englehart,et al.  A wavelet-based continuous classification scheme for multifunction myoelectric control , 2001, IEEE Transactions on Biomedical Engineering.

[16]  Adrian D. C. Chan,et al.  A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses , 2005, IEEE Transactions on Biomedical Engineering.

[17]  Othman Omran Khalifa,et al.  Neural Network Classifier for Hand Motion Detection from EMG Signal , 2011 .

[18]  Jorge J. Moré,et al.  The Levenberg-Marquardt algo-rithm: Implementation and theory , 1977 .

[19]  K. Krysztoforski,et al.  RECOGNITION OF PALM FINGER MOVEMENTS ON THE BASIS OF EMG SIGNALS WITH THE APPLICATION OF WAVELETS , 2004 .

[20]  F. K. Lam,et al.  Fuzzy EMG classification for prosthesis control. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[21]  R.N. Scott,et al.  A new strategy for multifunction myoelectric control , 1993, IEEE Transactions on Biomedical Engineering.