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Application of INA122 amplifier to measure of EMG signals

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper the amplifier to measure electromyographic (EMG) signals was developed. The device to recognize EMG signals was built with the use precision instrumentation amplifier INA122 made by BURR-BROWN Corporation. First, the solution was identified and the gain characteristics prepared. Next, the device was tested by measuring of EMG signals on biceps brachii muscle. Finally, the method of flex muscle identification was proposed.
Rocznik
Tom
Strony
7--12
Opis fizyczny
Bibliogr. 13 poz.
Twórcy
  • student kierunku Inżynieria Biomedyczna, Uniwersytet Zielonogórski, Zielona Góra
autor
  • Zakład Inżynierii Biomedycznej, Instytut Budowy i Eksploatacji Maszyn, Wydział Mechaniczny, Uniwersytet Zielonogórski, Zielona Góra, T.Klekiel@ibem.uz.zgora.pl
Bibliografia
  • [1] Micera S.: et. all, A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy technique,. Medical Engineering & Physics 21 (1999) 303–311.
  • [2] De Luca C.J.: Use of the surface EMG signal for performance evaluation of back muscles, Muscle and Nerve, 1994;16(2):210–6.
  • [3] Sutherland D.H., Olshen R., Cooper L., Woo SL-Y.: The development of mature gait, J Bone Joint Surg, 1980;62A(3):336–53.
  • [4] Sutherland D.H., Olshen R., Biden E.N., Wyatt M.P.: The development of mature walking, Oxford: MacKeith Press, 1988.
  • [5] Riek P., Bava P.: Recruitment of motor units in human forearm extensors. J Appl Physiol, 1992;68(3):100–8.
  • [6] Graupe D., Cline W.K.: Functional separation of EMG signals via ARMA identification methods for prothesis control purposes, IEEE Trans. Syst. Man Cybern., 1975;SMC–5(2):252–9.
  • [7] Graupe D., Salahi J., Kohn K.H.: Multifunctional prothesis and orthosis control via microcomputer identification of temporal pattern differences in single-site myoelectric signals, J. Biomed.Engng, 1982;JBE–4:17–22.
  • [8] Kelly M.F., Parker P.A., Scott R.N.: The application of neural networksto myoelectric signal analysis: a preliminary study, IEEE Trans. Biomed. Engng, 1990;BME–37(3):221–30.
  • [9] Zardoshti-Kermani M., Wheeler B.C., Badie K., Hashemi R.M.: EMG feature evaluation for movement control of upper extremity prostheses, IEEE Trans. Rehab. Engng, 1995;RE–3(4):324–33.
  • [10] Solnik S., Devita P., Grzegorczyk K., Koziatek A., Bober T.: EMG frequency during isometric, submaximal activity: a statistical model for biceps brachii, Acta of Bioengineering and Biomechanics, Vol. 12, No. 3, 2010.
  • [11] Solnik S., Devita P., Rider P., Long B., Hortobagyi T.: Teager–Kaiser Operator improves the accuracy of EMG onset detection independent of signal-to-noise ratio, Acta of Bioengineering and Biomechanics, Vol. 10, No. 2, 2008.
  • [12] http://www.burr-brown.com/, Datasheet.
  • [13] Micera S., Sabatini A. M., Dario P.: An algorithm for detecting the onset of muscle contraction by EMG signal processing, Med. Eng. Phys. 20 (1998) 211–215.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-710cda44-d2d0-41d2-8f77-285a49ea89fe
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