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Essay: EMG Sensor with Audiovisual Output: Building and Design Methodology

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EMG SENSOR

WITH

AUDIOVISUAL OUTPUT

Hassif Abdulahi Mustafa | EEE | November 28th, 2018 

Table of Contents:

Contents    Pages

Introduction 2

Background 2

Method 2

Circuit Design 3

Hardware Build 4

Software Build 5

Conclusion 6

References 6

Introduction

Electromyogram (EMG) signals come from the muscle cells, when these cells are electrically or neurologically activated by the nervous system, results in an electric potential.  These signals then cause contraction of the muscle which then results in the movement of the body the muscle is attached to representing neuromuscular activities. 2

In this scenario project, we were tasked with building an EMG sensor capable of detecting surface EMG signals from a muscle. Our system should include outputs of various sensors, such as audio, visual and motion (buzzer, LED bar and motor respectively). Our project will consist of a buzzer, LED and LED bar.

Background

EMG signals are electric potentials generated by muscle cells, after the sudden contraction of the muscle. The harder you apply the force the greater electric activity from the cells. This is somewhere between 5 to 6mV before amplification, these voltages also have noise in them from the skin and internal tissue. The signal is therefore filtered. After filtering of the signal, we get about 2 to 3mV which might seem small but very valuable data to researchers and engineers.

Method

Figure 3.1: overall system diagram

Our EMG sensor consisted of an instrumentation amplifier, a signal processing unit and the output unit which consisted of multiple forms like a LED, bar graph and a motor initially but due to the limitation of time the motor was abandoned.

The skin electrode sensor consisted of three electrodes, a positive, a negative and the third one is reference (neutral) which is attached to a bone. We need the reference sensor to reduce noise reduction, since the EMG signal produced by the muscle is very faint about 2mV, therefore in order to avoid the signal getting lost in the noise around it we use the reference electrode. We chose the instrumentation amplifier LMC660CN because of its low common mode gain and high input impedance.

CIRCUIT DESIGN

Instrumentation Amplifier

Figure 3.2: Instrumentation Amplifier

The Integrated Instrumentation Amplifier (LMC660CN) comes in a single package, this was Ideal for our project simplified the circuit design. Bearing in my mind that the signal we were going to detect was about 2 to 3mV we set the differential gain to about 1000. We made sure to make the gain variable to account for the different signal which ranges from person to person.

Signal Processing Unit

Figure 3.3: signal processing unit

After removing the common noise using the instrumentation amplifier the next step in the circuit design was to clean the signal by filtering it. We anticipated that an offset voltage of a few millivolts might have been picked up by the electrode which is amplified along with the EMG signal. Therefore, we added a high pass filter at the end of the instrumentation amplifier to eliminate it. There we used a second order high pass filter with a gain of 1 and a quality factor of 0.707.

After filtering the signal, the next step was to rectify it, to get the running average of the amplitude of the EMG signal, if we don’t do this the average would be zero. Therefore, tried to use diodes for rectification but since diodes need approximately 0.7V forward bias voltage to start conducting and our signal was much smaller than this. Because of this, we designed our rectifier in the super-diode configuration where our output is greater than zero, potentially swinging our signal positive.

The final stage was the integrator also called an envelope detector, this is where we smoothed our rectified signal. The integrator basically acts like a low pass filter with a gain of approximately 2. The cut-off frequency was given by fc=1/((2×π×R_f×C)).

HARDWARE BUILD

The hardware build was mainly segregated in two parts one was the actual circuit, which we built on a breadboard and then we soldered it on a Veroboard. The other was the outputs, which were LED, bar graph and a motor. We will be using Arduino Uno which is an ATMEGA328p Microcontroller based prototyping board. This is ideal because it is an open-source electronic prototyping platform that can be used with various sensors and actuators. Arduino Uno has 14 digital I/O pins out of which 12 pins are used in this project.

Figure 3.4: Arduino Uno/ATMEGA32P pin layout

Before building our circuit we initially tested it on breadboard, where we encountered a lot of problems, one of them was the clipping of sinewave at the output of the instrumentation amplifier, this was due to high voltage at the rail which saturated the output of the instrumentation amplifier, the solution was to cap it at +9V to -9V.

Once we tested every output of every stage in the circuit, we then embarked on integrating the outputs to the circuit using the Arduino. We attached the LED to the Arduino first according to the code, we then went on attach the bar graph and the motor to test it. Once we finished testing the outputs using the EMG signal, we got from a bicep, we then moved on to soldered it a stripboard we designed on the Veroboard.

The right side of the stripboard was the positive terminal with positive voltage supply and the left side was the negative voltage supply with +9V and -9v respectively.

I started by soldering the two LMC660CN instrumentation amplifiers IC chip into the stripboard and then followed the other components following the circuit design in figure 3.2. Making the connection between pins from the outputs to LMC660CN was the next logic step, so I decided to make the connections with an aluminum coil since aluminum has very good electrical characteristic. I made the connection following figure 3.3. After soldering all the components, I then proceeded by soldering buzzer, push button, LED bar graph and the LED.

SOFTWARE BUILD

Following the flowchart in figure 3.5. I made a code for the Arduino to control our output sensors, which were a light emitting diode (LED), a LED Bar Graph and speaker/buzzer. These outputs display how strong the EMG sensor is using different methods.

In the flowchart, in figure 3.5 the triangle shape represents the start of the program and boxes represent processes or state changes. In the setup() function we initialized the bar graph input pins by setting them to output, we then set the analog pin 12 to input so that we can read the output EMG signal of the circuit. In the void loop() we have two function that controls the LED bar graph and the speaker.

In the LED bar graph function first, we read the ADC input from the sensor. And then we mapped the input value to the output range, in this case, ten LEDs. Then you set up a for loop to iterate over the outputs. If the output's number in the series is lower than the mapped input range, we turned it on. If not, we turned it off.3

Figure 3.5: overall flowchart  

Conclusion

In conclusion, this technology is very important in fields of medicine, because it allows researchers data that wasn’t possible a couple of decades ago, now EMG signals are mainly used in clinical diagnosis and biomedical applications. The field of management and rehabilitation of motor disability is identified as one of the important application areas. This field is important because it helps people with disabilities, with applications such as prosthetics. This will undoubtedly improve the quality of life for people with psychomotor disabilities. 2

References

Walsh, Connor J. Herman, Maxwell. Sanah, Siddharth. Galloway, Kevin C. Polygerinos, Panagiotis. “EMG Controlled Soft Robotic Glove for Assistance During Activities of Daily Living.” Harvard Biodesign Laboratory, August 2015.

Raez, M.B.I.; Hussain, M.S.; Mohd-Yasin, F. (Mar 23, 2006). "Techniques of EMG signal analysis: detection, processing, classification and applications".Biol.

Arduino.cc. (2018). Arduino – BarGraph. [online] Available at: https://www.arduino.cc/en/Tutorial/BarGraph.

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