Development of a software module for feature extraction and classification of EMG signals

When the upper limb is amputated or lost, a prosthetic device play an important role in rehabilitation. Prosthetics are available in the form of myoelectric devices. These devices work by sensing the Electromyogram (EMG) signals, through electrodes, when the muscles in the upper arm move, this makes an artificial hand to move accordingly. EMG signals cannot be used directly in raw form to control any prosthetic, it needs to be processed before controlling any device. Different techniques are available for processing the EMG signals. Present paper has used the wavelet transform method to obtain the characteristics or features of EMG signals and then fuzzy controller has been applied on those features so that EMG signals can be used to make prosthetic device functional. NI Lab View, an efficient professional mathematical tool, has been used to process the EMG signals. EMG signals are acquired with the help of sensors placed on the skin and Data Acquisition System. Results of the experiment shows that the developed software module is an effective method to process the EMG signals and can be used for prosthetic devices.

[1]  P. Manimegalai.  HAND GESTURE RECOGNITION BASED ON EMG SIGNALS USING ANN , 2013 .

[2]  R. K. Tripathy,et al.  Artificial Neural Network based Body Posture Classification from EMG signal analysis , 2013 .

[3]  Qi Xu,et al.  Identification Scheme of Surface Electromyography of Upper Limb Movement , 2013, J. Networks.

[4]  F. Ebrahimi,et al.  Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  J. Kilby,et al.  Extracting Effective Features of SEMG Using Continuous Wavelet Transform , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  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.

[7]  Zhiguo Yan,et al.  The study on feature selection strategy in EMG signal recognition , 2013, 2013 ICME International Conference on Complex Medical Engineering.

[8]  P. L. Srinivasa,et al.  Development of two degree of freedom (DoF) bionic hand for below elbow amputee , 2013, 2013 IEEE International Conference on Electronics, Computing and Communication Technologies.

[9]  Ajat Shatru Arora,et al.  Multi-Class SVM Classification of Surface EMG Signal for Upper Limb Function , 2009, 2009 3rd International Conference on Bioinformatics and Biomedical Engineering.

[10]  A. Phinyomark,et al.  Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification , 2011 .

[11]  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.

[12]  P. A. Parker,et al.  A new fuzzy approach for pattern recognition with application to EMG classification , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[13]  Rajesh P. N. Rao,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. 1 Online Electromyographic Control of a Robotic , 2022 .

[14]  Sijiang Du,et al.  Temporal vs. spectral approach to feature extraction from prehensile EMG signals , 2004, Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration, 2004. IRI 2004..