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Reseach Article

Surface EMG Signal Amplification and Filtering

by Jingpeng Wang, Liqiong Tang, John E Bronlund
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 82 - Number 1
Year of Publication: 2013
Authors: Jingpeng Wang, Liqiong Tang, John E Bronlund
10.5120/14079-2073

Jingpeng Wang, Liqiong Tang, John E Bronlund . Surface EMG Signal Amplification and Filtering. International Journal of Computer Applications. 82, 1 ( November 2013), 15-22. DOI=10.5120/14079-2073

@article{ 10.5120/14079-2073,
author = { Jingpeng Wang, Liqiong Tang, John E Bronlund },
title = { Surface EMG Signal Amplification and Filtering },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 82 },
number = { 1 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 15-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume82/number1/14079-2073/ },
doi = { 10.5120/14079-2073 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:56:38.178388+05:30
%A Jingpeng Wang
%A Liqiong Tang
%A John E Bronlund
%T Surface EMG Signal Amplification and Filtering
%J International Journal of Computer Applications
%@ 0975-8887
%V 82
%N 1
%P 15-22
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Electromyographic (EMG) signals have been widely employed as a control signal in rehabilitation and a means of diagnosis in health care. Signal amplification and filtering is the first step in surface EMG signal processing and application systems. The characteristics of the amplifiers and filters determine the quality of EMG signals. Up until now, searching for better amplification and filtering circuit design that is able to accurately capture the features of surface EMG signals for the intended applications is still a challenging. With the fast development in computer sciences and technologies, EMG signals are expected to be used and integrated within small or even tiny intelligent, automatic, robotic, and mechatronics systems. This research focused on small size amplification and filtering circuit design for processing surface EMG signals from an upper limb and aimed to fix the amplifiers and filters inside a robotic hand with limited space to command and control the robot hand movement. The research made a study on the commonly used methodologies for EMG signal processing and circuitry design and proposed a circuit design for EMG signal amplification and filtering. High-pass filters including second-order and fourth-order with the suppression to low frequency noises are studied. The analysis, verification and experiment showed that a second-order high-pass filter can adequately suppress the low frequency noises. The proposed amplification and filtering circuit design is able to effectively clean the noises and collect the useful surface EMG signals from an upper limb. The experiment also clearly revealed that power line interference needs to be carefully handled for higher signal-noise-ratio (SNR) as a notch-filter might cause the loss of useful signal components. Commercial computer software such as LabView and Matlab were used for data acquisition software development and data analysis.

References
  1. De Luca, C. J. 2006. Electromyography. Encyclopedia of Medical Devices and Instrumentation, pp. 98-109.
  2. De Luca, C. J. 2002. Surface electromyography: Detection and recording. DelSys Incorporated.
  3. Day,S. 2002. Important factors in surface EMG Measurement. Calgary: Bortech Biomedical Ltd.
  4. INA128 Datasheet . 2005. Texas Instruments Incorporated.
  5. Zumbahlen, H. 2007. Phase Relations in Active Filters. Analog Dialogue, 41.
  6. Cosmanescu, A. , Miller, B. , Magno, T. , Ahmed, A. and Kremenic, I. Design and Implementation of a Wireless (Bluetooth®) Four Channel Bio-Instrumentation Amplifier and Digital Data Acquisition Device with User-Selectable Gain, Frequency, and Driven Reference. pp. 2053-2056.
  7. De Luca, C. J. , Donald Gilmore, L. , Kuznetsov, M. and Roy, S. H. 2010. Filtering the surface EMG signal: Movement artifact and baseline noise contamination. Journal of Biomechanics, vol. 43, no. 8, pp. 1573-1579.
  8. Smith, L. H. , Hargrove, L. J. , Lock, B. A. and Kuiken, T. A. 2011. Determining the Optimal Window Length for Pattern Recognition-Based Myoelectric Control: Balancing the Competing Effects of Classification Error and Controller Delay. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol. 19, no. 2, pp. 186-192.
  9. Analysis of the Sallen-Key Architecture. 1999. Texas Instruments Incorporated.
  10. Guanglin, L. , Yaonan, L. , Zhiyong, Z. , Yanjuan, G. and Rui, Z. Selection of sampling rate for EMG pattern recognition based prosthesis control. pp. 5058-5061.
  11. Cipriani, C. , Antfolk, C. , Controzzi, M. , Lundborg, G. , Rosen, B. , Carrozza, M. C. and Sebelius, F. 2011. Online Myoelectric Control of a Dexterous Hand Prosthesis by Transradial Amputees. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol. 19, no. 3, pp. 260-270.
  12. Hargrove, L. J. , Scheme, E. J. , Englehart, K. B. and Hudgins, B. S. 2010. Multiple Binary Classifications via Linear Discriminant Analysis for Improved Controllability of a Powered Prosthesis. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, vol. 18, no. 1, pp. 49-57.
  13. Oskoei, M. A. and Huosheng, H. 2008. Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb. Biomedical Engineering, IEEE Transactions on, vol. 55, no. 8, pp. 1956-1965.
  14. Phinyomark, A. , Nuidod, A. , Phukpattaranont, P. and Limsakul, C. 2012. Feature extraction and reduction of wavelet transform coefficients for EMG pattern classification. Elektronika ir Elektrotechnika, vol. 122, no. 6, pp. 27-32.
  15. Jun-Uk, C. , Inhyuk, M. and Mu-Seong, M. 2006. A Real-Time EMG Pattern Recognition System Based on Linear-Nonlinear Feature Projection for a Multifunction Myoelectric Hand. Biomedical Engineering, IEEE Transactions on, vol. 53, no. 11, pp. 2232-2239.
  16. Zhang, X. and Zhou, P. 2012. Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes. Journal of Electromyography and Kinesiology, vol. 22, no. 6, pp. 901-907.
  17. Khushaba, R. N. , Kodagoda, S. , Takruri, M. and Dissanayake, G. 2012. Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals. Expert Systems with Applications, vol. 39, no. 12, pp. 10731-10738.
  18. Hosking, R. H. 2012. Critical Techniques for High-Speed A/D Converters in Real-Time Systems.
  19. Fukaya, N. , Toyama, S. , Asfour, T. and Dillmann, R. Design of the TUAT/Karlsruhe humanoid hand. pp. 1754-1759.
  20. Yacoub, S. , Raoof, K. and Eleuch, H. Filtering of Cardiac and Power Line in Surface Respiratory EMG Signal.
  21. Golabbakhsh, M. , Masoumzadeh, M. and Sabahi, M. F. 2011. ECG and power line noise removal from respiratory EMG signal using adaptive filters. Majlesi Journal of Electrical Engineering, vol. 5, no. 4.
  22. A. B. Sankar, D. Kumar, and K. Seethalakshmi, "Performance study of various adaptive filter algorithms for noise cancellation in respiratory signals," Signal Processing: An International Journal (SPIJ), vol. 4, no. 5, pp. 267, 2010.
  23. De Luca, C. J. , Kuznetsov, M. L. , Gilmore, D. and Roy, S. H. 2012. Inter-electrode spacing of surface EMG sensors: Reduction of crosstalk contamination during voluntary contractions," Journal of Biomechanics, vol. 45, no. 3, pp. 555-561.
Index Terms

Computer Science
Information Sciences

Keywords

EMG signals data acquisition signal processing.