REAL TIME EMG SIGNAL PROCESSING

INDIAN INSTITUTE OF INFORMATION TECHNOLOGY ALLAHABAD

PROJECT REPORT ON

REAL TIME EMG SIGNAL PROCESSING

Supervised by :- Dr Akhilesh Tiwari

Presented by:Smriti Sinha ibm2013005 Raviranjan Kumar ibm2013010 Deepak Sonkar ibm2013020 Stavant Arya ibm2013039 Swapnil Singh ibm2012002

CANDIADATES DECLARATION We hereby declare that the work presented in this project report entitled “Real time emg signal processing”, being submitted as a part of Mini Project Evaluation at Indian Institute of Information Technology, Allahabad, is an authenticated record of our original work under the guidance and supervision of Dr. Akhilesh Tiwari.

Date: 17, March, 2016 Place: Indian Institute of Information Technology, Allahabad

CERTIFICATE This is to certify that the statement made by the candidates is correct to the best of my knowledge and belief. The project entitled “Real time emg signal processing” is a record of candidates work carried out by them under my guidance and supervision.

Dr. Akhilesh Tiwari Assistant Professor Indian Institute of Information Technology, Allahabad Date:-

ACKNOWLEDGMENT It is pleasure to express thanks to Dr. Akhilesh Tiwari for the encouragement and guidance throughout the course of this project. We are grateful to Dr. Akhilesh Tiwari for his even willingness to give us valuable advice and direction; whenever we approached him with a problem. We are thankful to him for providing immense guidance for this project. We are very thankful to all the technical and non-technical staffs of the college for their assistance and co-operation.Last but not the least we wish to avail ourselves of this opportunity, express a sense of gratitude and love to our friends and our beloved parents for their manual support, strength, helps and for everything.

By:Smriti Sinha Raviranjan Kumar Deepak Sonkar Stavant Arya Swapnil Singh

ibm2013005 ibm2013010 ibm2013020 ibm2013039 ibm2012002

Content 1.Objective 2.Motivation 3.Introduction 4.Methodology 5.Circuit diagram and Components Used 6.Working 7.Matlab simulation 8.Conclusion and future Scope 9. Future work and work till now 10.References

1.OBJECTIVE The aim of the project to design Real time Electromyography measuring Device using Amplifiers and Arduino and send signal through Bluetooth and develop android app for visualization of data/signal on other device .

2.MOTIVATION The main motivation of this work are, to talk about the signal of EMG and predict about muscular related problems, alerts the patient for any problem related with muscles and Improve the quality of designs of prosthesis and hence enhance the quality of life for the patient. To reduce it’s complexity and making it more feasible and reliable for practical application. •

3.INTRODUCTION Small electrical currents are generated by muscle fibres prior to the production of muscle force. These currents are generated by the exchange of ions across muscle fibres membranes, a part of the signaling process for the muscle fibres to contract. The signal called the electromyogram (EMG) can be measured by applying conductive elements or electrodes to the skin surface, or invasively within the muscle. Surface EMG is the more common method of measurement, since it is non-invasive and can be conducted by personnel other than Medical Doctors, with minimal risk to the subject [1A]. Measurement of surface EMG is dependent on a number of factors and the amplitude of the surface EMG signal (EMG) varies from the uV to the low mV range. The amplitude and time and frequency domain properties of the EMG signal are dependent on factors such as [2A] • The timing and intensity of muscle contraction • The distance of the electrode from the active muscle area • The properties of the overlying tissue (e.g. thickness of overlying skin and adipose tissue) • The electrode and amplifier properties • The quality of contact between the electrode and the skin In most cases, information on the time and intensity of muscle contraction is desired. The remainder of the factors only exacerbates the variability in the EMG records, making

interpretation of results more difficult. Nevertheless, there are methods to reduce the impact that non- muscular factors have on the properties of the EMG signal. For example, much of this variability in the EMG signal can be minimized through [1]: • using the same electrodes and amplifier (i.e. same signal conditioning parameters) • ensuring consistency in the quality of contact between the electrodes and the skin

Measuring and accurately representing the EMG signal depends on the properties of the electrodes and their interaction with the skin, amplifier design, and the conversion and subsequent storage of the EMG signal from analog to digital form (A/D conversion). The quality of the measured EMG is often described by the ratio between the measured EMG signal and unwanted noise contributions from the environment. The goal is to maximize the amplitude of the signal while minimizing the noise. Assuming that the amplifier design and process of A/D conversion exceed acceptable standards the signal to noise ratio is determined almost exclusively by the 2 electrodes, and more specifically, the properties of the electrode – electrolyte – skin contact [2].

 Sources of Noise Before we can develop strategies to eliminate unwanted noise we must understand what the sources of noise are. The two types of noise are ambient noise and transducer noise. 

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Ambient noise Ambient noise is generated by electromagnetic devices such as computers, force plates, power lines etc. Essentially any device that is plugged into the wall A/C (Alternating Current) outlet emits ambient noise. This noise has a wide range of frequency components, however, the dominant frequency component is 50Hz or 60Hz, corresponding to the frequency of the A/C power supply [2]. Transducer noise

Transducer noise is generated at the electrode – skin junction. Electrodes serve to convert the ionic currents generated in muscles into an electronic current that can be manipulated with electronic circuits and stored in either analog or digital form as a voltage potential. There are two types of noise sources that result from this transduction from an ionic to an electronic form: • D/C (Direct Current) Voltage Potential: caused by differences in the impedance between the skin and the electrode sensor, and from oxidative and reductive chemical reactions taking place

in the contact region between the electrode and the conductive gel. • A/C (Alternating Current) Voltage Potential: generated by factors such as fluctuations in impedance between the conductive transducer and the skin. One effective method to decrease impedance effects is to use Ag-AgCl electrodes. This electrode consists of a silver metal surface plated with a thin layer of silver chloride material [2]. The goal with EMG measurements is to maximize the signal to noise ratio. Technological developments have decreased the level of noise in the EMG signal. The most important development was the introduction of the bipolar recording technique. Bipolar electrode arrangements are used with a differential amplifier, which functions to suppress signals common to both electrodes. Essentially, differential amplification subtracts the potential at one electrode from that at the other electrode and then amplifies the difference [3]. Correlated signals common to both sites, such as from power sources and electromagnetic devices, but also EMG signals from more distant muscles are suppressed. Moreover, the D/C components such as the over-potential generated at the electrode skin junction will be detected with similar amplitude (see below) and will therefore be suppressed [1]. In contrast, signals from muscle tissue close to the electrodes will not be correlated and will be amplified. The advent of bipolar recordings with differential pre-amplification has enabled the recording of the full EMG bandwidth while increasing the spatial resolution (i.e. the size of the recording area). This also has the effect of increasing the signal to noise ratio [1]. One remaining factor is how the quality of the electrode – skin contact impacts the process of differential amplification in bipolar EMG measurements. The electrode – skin contact is quantitatively defined by the resistance of the skin and underlying tissues, in addition to the capacitance of the electrodes. It is commonly called electrode – skin impedance [3].

4.Methodology The whole project is divided into two parts, hardware and software. Firstly, We have simulated emg data on Matlab .Then code is developed in Arduino. It take the data from the muscle through electrode to the differential amplifier and then it send input to microcontroller via operational amplifiers. The Microcontroller is a small computer on a single integrated circuit containing a processor core, memory, and programmable input output peripherals. Microcontroller is connected to the computer where it send the output signal through circuit. This concept consists of three major systems: - Differential amplifier, operational amplifiers and Microcontroller.

5. Circuit diagram and Components Used

CIRCUIT DIAGRAM [A] Components used… 3*TL072 IC Chips 1*INA128 IC Chip 1*Aurdinouno (atmega 8) 3 * Cables for EMG 3*Electrode 2* 9v Battery Capacitors- 2*1.0 micro faraday Tant &1* 0.01 micro faraday Ceramic Disc 1*1.0 micro faraday Ceramic Disc Resistors 3 * 150 Kohm & 2*1 Mohm & 6 * 10 Kohm & 1*100 Kohm Trimmer 2 * 1N4148 Diode Jumper Wires 3*Alligator Clip Cables & 2 * Bread board

TL072 IC Chip (operational amplifier)

PIN CONFIGURATION OF TL072 [E] The TL072 JFET operational amplifier family is designed to offer wide selection than any previously developed operational amplifier family.Each of these JFET-input operational amplifiers incorporates well-matched, high-voltage JFET and bipolar transistors in a monolithic integrated circuit. INA128 IC Chip (instrumentation amplifier)

PIN CONFIGURATIO OF INA128 [D] The INA128 is low-power, general purpose instrumentation amplifier offering excellent accuracy. The versatile 3-op amp design and small size make this amplifier ideal for a wide

range of applications. Current feedback input circuitry provides wide bandwidth even at high gain.

Ardiuno Uno

ARDIUNO UNO BOARD THAT WE ARE USING IN OUR PROJECT The Ardiuno is a Microcontroller based on the ATmega8. It has 14 digital input and output pins (of which 6 can be used as PWM outputs), 6 analog input, a 16 MHz quartz, a USB connection, a power jack, an ICSP header and a reset button. It contains all the things need to support the Microcontroller; simply connect it to a computer with a USB cable or power it with an AC to DC adapter or battery to get started.

5. Working    

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First of all we connect two 9v battery in series to get 18v power supply for our circuit Next step is signal acquisition of our EMG circuit which we will use to measure the body’s nervous system’s electrical impulses used to activate muscle fibers. First we get INA128 IC Chip insert it in our bread broad as illustrated above. The INA128 is an instrumentation amplifier which measure and amplify the very small voltage difference between the two electrodes we placed on the muscle. Next we start with two series of amplification the first with inverting amplifier with gain of -15. To do this we using TL072 IC chip with 150kohm and 10kohm resister (G = 150/10). Next we are going to add a capacitor to AC couple the signal. AC coupling is useful to removing DC error offset in a signal. Now we are going to add an active high pass filter to get of any DC offset and low frequency noise. To do this we used 150kohm resistor and 0.01micro faraday capacitor. We will rectifying the signal using an active full-wave rectifier. Our rectifier will take the negative portion of our signal and turn it positive so the entire signal falls within the positive voltage region. We will this coupled with a low pass filter to turn our AC signal to DC voltage. In this last phase of circuit assembly we will be using active low pass filter to filter out the humps of our signal to smooth signal for our microcontroller. That’s the end of the filter circuit. However, since this is an active filter, there is a side effect of inverting the signal. We will need to invert the signal one more time using another inverting amplifier circuit with a trimmer configured as a variable resistor. By using a screw driver and turning the trimmer, you will be able to adjust the gain of your signal to account for different signal strength from different muscle group. Now remove the hair from the muscles from which we have to take the EMG data, because hair may create the unnecessary noise. Put two electrode on the two points on muscle to get the difference of voltage and third on to the elbow as ground. Now connect circuit to Arduino and then finally to the computer, upload the Arduino code on Arduino IDE then run and plot the signal from serial plotter.

6.Matlab simulation % plote data simply for that we use transformation to make simple % to calculation % (intiger) as sampling frequency a = load('C:\Users\ONY\Desktop\emg.txt'); fs = 700; y = fft(a(:,1),fs); %figure %plot(a) %xlabel('time in ms') %ylabel('amplitude') % revove any dc offset of the signal a2 = detrend(a); % a2 is the signal without dc offset rec_a = a2; %rec_a = abs(a2); figure plot(rec_a) xlabel('sample') ylabel('rectifired signal') %figure %stem(abs(y)); %xlabel('frequancy') %ylabel('amplitude') % now for linear envelop of the signal % for it we need low pass filter of a specific cut off frequency [x,y] = butter(7,2/700,'low') % ordder 2 assume on me, cutoff freq.10as per you, sampling frequiency 1024, satates offilter low, type of filter butter %data_out = filter(x,y,rec_a); %figure %plot(data_out) % we use filtfilt comand to optain linear envelop filter_a = filtfilt(x,y,rec_a); figure plot(filter_a) xlabel('sample number') ylabel('low pass filtered emg signal')

RESULT OF OUR MATLAB SIMULATION

8. Conclusion and future Scope The objectives of the project are successfully achieved. All of the functions specified in this design have been implemented and their result has been seen. Output is coming with some noise so some modification is require further.This system is reliable, quick responsive and cost effective. It is easy to operate, very efficient and user friendly device. An ordinary man can operate this device with minimal knowledge of the unit. System has a huge potential or research to make it more versatile for medical observation activities.As part of future enhancement of this EMG device, it has vast uses to develop new advanced modern design equipment. And a very useful thing is it can be easy to interface with other design system by doing small modifications in the given circuit design. The design can be interfaced with advanced Bluetooth techniques, medical and academic purpose.

9. Future work and work till now Till now we worked up to Matlab simulation of downloaded EMG data, and also take the data from living muscle by electrode and electronic circuit and plot them using Ardiuno on computer screen. In the second half we will be working to develop an Android app and connect the Wireless module to the EMG circuit and send the data to another devices through wireless on the Android app.

10. References [A http://www.instructables.com/id/Muscle-EMG-Sensor-for-a-Microcontroller/ [B] http://health.uottawa.ca/biomech/courses/apa4311/semg.pdf [C] http://circ.ahajournals.org/content/34/4/649.full.pdf [D] http://www.ti.com/lit/ds/symlink/ina128.pdf [E] http://www.ti.com/lit/ds/symlink/tl072.pdf [F] http://projectpoint.in/ [G] https://en.wikipedia.org/wiki/Electromyography [H] https://physionet.org/physiobank/database/emgdb/ [I] http://www.rami-khushaba.com/electromyogram-emg-repository.html [J] https://www.arduino.cc/en/Main/ArduinoBoardU [K] https://physionet.org/physiobank/database/emgdb/

Other general helps  

Electromyography Fundamentals (Gregory S. Rash, EdD) [1A] Important Factors in Surface EMG Measurement (By Dr. Scott Day) [2A]

Research papers Filter optimization of EMG signal using Matlab,ChanderpalSharmaa, ManojDuhanaand Dinesh Bhatiab, 2010 [1] Surface EMG Signal Amplification and Filtering,Jingpeng Wang, Liqiong Tang, John E Bronlund, November 2013)

[2]

Surface Electromyography Signal Processing and Classification Techniques, (Rubana H. Chowdhury 1,*, Mamun B. I. Reaz 1,2013 [3]