Prototipo para obtencion y utilizacion de Señales electromiograficas

8/13/2019 Prototipo para obtencion y utilizacion de Seales electromiograficas

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UNIVERSIDAD DE LA SALLE. Alejandro Prez, Csar Navarro, Edward Quiroga

SIGNAL AND SYSTEM ANALYSISELECTROMYOGRAPHY SIGNALS

Perez Alejandro, Navarro Cesar, Quiroga Edwarda

Abstract-Now we are going to present the first advance of

project at signal and system analysis, in this project we

want acquire the signals of an arm and this analyzer this

signal, after this signal we are going to use for move a

robotic arm. In this report we find a proposal of work, in

this proposal we show an introduction with the objectives

that we need do, a short theoretical framework when we

will can the concepts that help us to know how do the

project, and problematic that we need solve, also a

methodology to follow with materials that we need use for

the realization of this project, we are going to do this

project and in this document we find an alternative to do

for solve the practice.

I. INTRODUCTIONWhen started with the study of signals we needknow that this study does not a study of board andmust be a study practical, with componentstheoretical and practical, so in this class we needknow the comportment of the signals and that bestthat the signals of the body that is a team muchstudied, the signals is a language so big that wecan analyzer anything, for this reason the analysisof body and the implementation of this knowledge

is so important in our profession and in theknowledge area.

II. PROBLEMThe problem in this project is acquire analyzerand use the signal of an robotic arm, we need dotreatment to this signal because the signals in theenvironmental generate noise that affect thecomportment of the signal, that the signalacquired is very different to the real signal, isnecessary use filters and amplifiers that help us toimprove the quality of the signal and can use in anapplication most robust as a robotic arm; thissignal we need acquire with the help to electrodeand a DAQ, network analyzer or a oscilloscopethat is help us to acquire signals and analyzers inthe computer.

III. METHODOLOGYFor this project we are going to the nextmethodology that help us to do the project, fist weneed Investigate about mimeographed signals andits ideal comportment and real comportment, alsowe need know a form for acquire the signal foranalyzer, we need amplifier the signal, with anoperational transistor or a circuit of transistors,this depends of the frequency of the signal in thearm, but also we can filter with MATLAB that is a

software very flexible, after of acquire, amplifierand filter the signal we need use this signal with acontroller, for example a PLC or microcontrollerthat help us to controller the robotic arm with thesignal that we send since of the computer, with thesoftware MATLAB.

IV. SOFTWARE TO USE MATLAB 2010 LabviewV. MATERIALS TO USE Computers Electrodes Cables INA128 Bread board Various electronics componentsVI. THEORETICAL FRAMEWORK

Electromyography signalsThe studies of the signals EMG was has a greatimportance and interesting in the technologicadvances because this advances help them to thepeople that have some one problem and incapacityfor the realization of activities with someone

These signals began in the 70's with Graupe andCline, in the 80's with Doershuck I applycorrelation techniques, in the 90's with Kang, the


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use cepstral coefficients, in 99 NASA technicaldevelopment pattern recognition EMG throughneural networks and hidden Markov models, in2002 Dunlop Fergusson and characterizationtechniques developed EMG signals, weredeveloped in 2003 also works for the prosthesis

training, and prediction of fatigue in the muscles.

EMG signals have a long time which has beendevoted to studying. But the EMG are electricalsignals obtained from a muscle contraction in theperiod, thanks to these muscle signals can analyzeand identify that arm going to use. In the project,it is necessary to buy electrodes, amplifiers, filtersand other elements that are necessary for thedevelopment of the project.

It is essential to identify the most ideal electrodes

to capture the EMG signals as these are veryfragile and can get from 0 6mV and a frequencyfrom 0 to 520Hz where most energy isconcentrated between 50 and 150Hz.

Electrodes:

The electrodes are sensors to use in the project arebipolar surface electrodes. The most importantaspects regarding the sensors are: the type ofelectrode, distance between electrodes, and thelocation of the electrodes.

Graph 1: Electrodes

Filters:

Electronic filters are analog circuits whichperform signal processing functions, specificallyto remove unwanted frequency components fromthe signal, to enhance wanted ones, or both.

Electronic filters can be: passive or active, analogor digital high-pass, low-pass, band pass, band-reject (band reject; notch), or all-pass; discrete-time (sampled) or continuous-time linear or non-linear infinite impulse response (IIR type) or finiteimpulse response (FIR type) The most common

types of electronic filters are linear filters,regardless of other aspects of their design. See thearticle on linear filters for details on their designand analysis.

Filter Band pass

A band-pass filter is a device that passesfrequencies within a certain range and rejects(attenuates) frequencies outside that range;Optical band-pass filters are of common usage.An example of an analogue electronic band-passfilter is an RLC circuit (a resistorinductor

capacitor circuit). These filters can also be createdby combining a low-pass filter with a high-passfilter.

Graph 2: Pass band Filter

Band pass is an adjective that describes a type offilter or filtering process; it is to be distinguishedfrom pass band, which refers to the actual portionof affected spectrum. Hence, one might say "Adual band pass filter has two pass bands." A bandpass signal is a signal containing a band offrequencies away from zero frequency, such as asignal that comes out of a band pass filter.

An ideal band pass filter would have a completelyflat pass band (e.g. with no gain/attenuation

throughout) and would completely attenuate allfrequencies outside the pass band. Additionally,the transition out of the pass band would beinstantaneous in frequency. In practice, no bandpass filter is ideal. The filter does not attenuate allfrequencies outside the desired frequency rangecompletely; in particular, there is a region justoutside the intended pass band where frequenciesare attenuated, but not rejected. This is known as

V1

L1

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C1

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the filter roll-off, and it is usually expressed in dBof attenuation per octave or decade of frequency.Generally, the design of a filter seeks to make theroll-off as narrow as possible, thus allowing thefilter to perform as close as possible to itsintended design. Often, this is achieved at the

expense of pass-band or stop-band ripple.The bandwidth of the filter is simply thedifference between the upper and lower cutofffrequencies. The shape factor is the ratio ofbandwidths measured using two differentattenuation values to determine the cutofffrequency, e.g., a shape factor of 2:1 at 30/3 dBmeans the bandwidth measured betweenfrequencies at 30 dB attenuation is twice thatmeasured between frequencies at 3 dBattenuation.

Outside of electronics and signal processing, oneexample of the use of band-pass filters is in theatmospheric sciences. It is common to band-passfilter recent meteorological data with a periodrange of, for example, 3 to 10 days, so that onlycyclones remain as fluctuations in the data fields.In neuroscience, visual cortical simple cells werefirst shown by David Hubel and Torsten Wiesel tohave response properties that resemble Gaborfilters, which are band-pass.

Movement to do:

For the realization of this project is necessaryobtain the electromyography signals with variousmovement of the hand, then goes to presented themovement necessaries for the realization of thisproject:

Graph 3: Hand open

Hand open: will wait that in this first movementwill obtain the basic signal that allow know an ofthe various signals in this project.

Graph 4: Hand close

Hand Close: will wait that in this first movementwill obtain the basic signal that allow know an of

the various signals in this project, this signal isdifferent and stronger that the movement with thehand open, for this reason in this movementexpected a signal higher that first signal.

Graph 5: Hand resting

Hand resting: In this position of the hand isexpected a signal very small that can considerateas noise does not desired.

Possible position of the electrodes:

Graph 6: Hand resting.


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Graph 7: Hand in Forearm

Integrated circuit INA128:

The integrated circuit INA128 is a circuit ofinstrumentation amplifier that allow obtain a gainvery big of a signal very small, is a circuit verydelicate because has current very short, however,to continuation the schematic of the circuitINA128:

Graph 8: Schematic of INA128

V. PROCEDURE

For this project is necessary know thecomportment of the electromyography signals asthat these signals are very small so the noise is aproblem necessary for right in when acquire thesignal, for the amplification of the signals isnecessary select an amplifier that does not retainnoise in the frequency of the electromyographysignals, for this reason is selected an operationalamplifier INA128 that amplifies signals verysmall; after of selected an amplifier that generate aamplification with an amplitude measurable isnecessary do a filter for eliminate the noise insignal, in this case is selected a filter band passinto 40 and 80 Hz with a resonance frequency of60 Hz that is the normal frequency of the

electromyography signals.

The calculus of the filter in ANEX 1

After of selected the filter and do theamplification of the signal is necessary obtain thesignals in the computer with a board of acquisitionthat in this case is the National Instrument DAQUSB 6212 with the help of Labview that is a

software flexible and use in the industry becausehave robust applications and with easyprogramming.

After of do the acquisition of the signals isnecessary controller a robotic arm with the signals

electromyography, but before to this is obtain adata base of the signals electromyography andaccording of signal send to robot the robot does amovement similar to the hand.

Now we are going to the second part of the projectthat is the acquisition the signals in the computerfor be treat with MATLAB, in MATLAB we aregoing to a new filter in the signal for obtain asignal as clean as possible, the acquisition of thesignal will with the DAQ USB 6212 that is aflexible and easy to use.

To next going to watch the advance of the projectwhen is possible see that the first part of project isterminated and with the approval of the engineeris continue with the second part of the project thatalthough there is an advance does not a formaladvance.

The advanced of the project, with first part:

Graph 9: Circuit in the Bread board

In the graph 9 is possible see the circuit in thebread board that help to obtain the signalelectromyography.


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Graph 10: Signal electromyography

In the graph 10 is possible see the signalelectromyography.

Graph 11: position electrodes

In the graph 11 is possible see the position of theelectrodes.

Graph 12: FFT of the signal

In the graph 12 is possible see the FFT of thesignal when the fundamental harmonic is in 59Hz.

Graph 13: electromyography signal

The diagram block of the Project is:

Graph 14: Diagram of block

The simulation of the filter is:

Graph 15: Comportment of a filter band pass


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If we use a filter band pass is necessary use valuesof 1mF and 0.1 ohm, which are values absurd, forthis reason we use a filter pass low and thecomportment is:

Graph 16: Comportment a filter low pass

With a filter low pass is possible does a treatmentof the signal because the values of its components

are 100uF and 83 ohms, that is possible use thisvalues of components, when we use the filter weobtain the next comportment:

Graph 17: comportment of the signal with filter

low pass

If we see the comportment of the signal with afilter low pass is possible see a signal withoutnoise and more clean that the original signal.

VII. COSTSmaterial precio

electrodo 2000

INA128 25000

protoboard 10000

caimanes 3000

locin 7000

tarjeta de adquisicin --

total53000

VIII. BIBLIOGRAPHY

BAREA NAVARRO, R.Instrumentacin BiomdicaElectromiografa. DepartamentoElectronica, Universidad Alcal Vol. 14,pp. 136-159 2005 [1]

BARREDA, Luis E. Electromigrafo.Laboratorio de Comunicaciones,Universidad Nacional del Mar del Plata.Pag 84-89, 2005 [2]

CARLO J, De Luca. Surfaceelectromyography: Detection andRecording. Delsys Inc, vol.1, pp. 37-54,2002. Available from the Internetat: [3]

BRONZINO, Joseph. BiomedicalEngineer Handbook. Second Edition.Pag, 1040-1340, 2000. [4]

Clnica Universitaria Universidad deNavarra. Electromiografa. Disponibledesde internet en:


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Politcnico Nacional (IPN), vol.1, pp.37-93.Mxico.1995. [7]

INSTITUTO COLOMBIANO DENORMAS TECNICAS YCERTIFICACION. Trabajos escritos:

presentacin y referencias bibliogrficas.Sexta actualizacin. Bogot:INCONTEC, 2008. 11 [8]

JAIMES VEGA, Diego F. ORDUAGUERRERO, Hernan.Mdulo de tratamiento de sealeselectromiografas,MOTSEL.

Bucaramanga. Universidad PontificiaBolivariana. TE121/J26, 2005. JOSEPH GOOD, ArthurEbersten. Electrodiagnosis ofneuromuscular diseases. London.

Editorial Williams & Wilkins. Terceraedition. 1983 U.S.A. [9]

LEON VARGAS, FABIAN M.CAMACHO NAVARRO, JHONATANDiseo de una interfaz electrnica para elreconocimiento de patrones EMG paraprtesis de mano. Universidad Industrialde Santander. 2008. [10]

DISEOYCONSTRUCCIONDEUNSISTEMAEGM.COM Alonso A., SnchezH., Hurtado E., Steinz D., Liptak L.,

Entrenador Mioelctrico de PrtesisPara Amputados de Brazo yMano, Hospital clnico Universitario,

Universidad de Valladolid. MapfreMedicina. Vol. 13, pp. 1119,2002. [11]

Ferguson S., Dunlop G Reg., GraspRecognition From Myoelectric Signals.

Australian conf. on Robotics andAutomation, Auckland, 2002.[Fecha de

consulta nov 2006]. Disponible enhttp://www.araa.asn.au/acra/acra2002/Papers/FergusonDunlop.pdf [12]

Journal Advances in Systems and

Informatics, Vol. 1364 No. 1, Junio de 2007

Englehart K., Hudgins B., and Parker P.,Improving Myoelectric Signal

Classification Using Wavelet Packets andPrincipal Component Analysis. Proc. Of

21st Annual Int. Conf. of the IEEE onEng. In Med. And Biol. Soc. Atlanta1999. [13]

Lowery M., Stoykov N., Taflove A., yKuiken T., Tesis profesional A Mltiple

ayer Finite Element Model of the SurfaceEMG Signal. IEEE Transactions onBiomedical Engineering, vol. 49, no. 5,pp. 446454,May 2005. [14]

Zhang X., Yang Y., Xu X. Zhang J., GaoZ., Hu T., Chen T., Chen Z. ClinicalDetection and Movement Recognition ofNeuro Signal. Journalof ZhenjianUniversity SCIENCE. pp. 272279.2004. [15]

Graupe D, Salahi J, Kohn KH.,Multifunctional Prosthesis and OrthosisControl Via Microcomputeridentification of temporal patterndifferences in singlesiteMyoelectric signals. J Biomed Eng. Vol.

4, pp. 17 22, 1982. [16]
http://www.araa.asn.au/acra/acra2002/Papers/FergusonDunlophttp://www.araa.asn.au/acra/acra2002/Papers/FergusonDunlophttp://www.araa.asn.au/acra/acra2002/Papers/FergusonDunlophttp://www.araa.asn.au/acra/acra2002/Papers/FergusonDunlop


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ANEX 1

CALCULOS FILTER

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Prototipo para obtencion y utilizacion de Señales electromiograficas

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