Voice Recognition Based Automation System for Medical Applications and for Physically Challenged Patients

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1976
Voice Recognition Based Automation System for Medical Applications
and For Physically Challenged Patients
Sanu Kumar Das1, Vitthal Rathod2, Akhilesh Yadav.B3
1Sanu Kumar Das, Dept. Of Electronics & Telecommunication, DYPCOE, Maharashtra, India
2Vitthal Rathod, Dept. Of Electronics & Telecommunication, DYPCOE, Maharashtra, India
3Akhilesh Yadav, Dept. Of Electronics & Telecommunication, DYPCOE, Maharashtra, India
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Abstract – Voice is the most effective way of
communication among human being. This, way of
communication, can also be a useful interface to interact with
machines. Therefore the dependency of voice recognition
system has increased greatly in recent year. There are
different methods to speech recognition like Hidden Markov
Model (HMM), Hybrid Hidden Markov Model (ANN), etc.
This paper presents the prototype of voice recognition based
automation system for the physically challenged people
suffering from quadriplegia or paraplegia (who cannot move
their body parts but can speak and listen) to control the
various devices and can control the bed movement just by the
voice commands according to his/her desire and comfort. The
proposed model has a voice recognition model, Arduino uno
microcontroller, relay circuit for LED & Buzzer and a motor
for adjustable bed. The voice recognition model should be
trained first & data should be stored before it can be used to
recognize the commands. Once it recognized voice command
the Arduino will control the respected load withthehelpofthe
relay circuit. The adjustable bed can be operated at different
modes as per the user’s requirement. The accuracy of voice
recognition model is measured in different conditions. The
results show the system can provide great help to the
paralyzed people without any third person’s assistances.
Key Words: Voice Recognition,ANN,AutomationSystem for
Paralyzed People, Arduino Uno, Motor, Buzzer, LED.
1. INTRODUCTION
Voice is the most effective and natural way to communicate.
Human being also wants to have a similar natural, easy and
effective way of communication with machines. Therefore
they prefer voice as a media to interact with devices rather
than using any other hectic interfaces like mouse and
keyboards. But the voice is a complex phenomenon as the
human vocal pitch and articulators, being the biological
organs, are not under our control and not same every time.
Voice Recognition or Automatic Speech Recognition (ASR)
plays an important role in human being and machine
interaction. Voice recognition uses differentmethodologyto
recognize the word and to convert voice signals into the
sequence of words bymeans ofanalgorithmimplemented as
a computer program. Different techniques are used for this
process, like LPC, MFCC along with ANN. Voice recognition
systems are capable of understanding of different languages
and different of words under functional environment.
Voice signal provides two important types of information:
[1] Content of Voice and [2] Identity of speaker.
Voice recognition automation system can be a used for
various applications.[1] It can be used for home
automation[2] It can be used for paralyzed patients to
control multiple devices.[3] It has also many applications
like telephone directory assistance, automatic voice
translation into foreign languages.
2. VOICE RECOGNITION PROCESS
The process of voice recognition is complexanda hecticjob.
The figure 1given below shows following steps involved in
the process of voice recognition.
2.1 Voice Acquisition
Voice is the effective form of human communication. In this
process, the voice of the speaker is received in the form of
waveform. There is lots of software available whichareused
to record the voice of human being. In this we arestoringthe
voice signal in the form of “.mat” The acoustic atmosphere
and receiving equipment can have great effect on the voice
generated. Sometime we have background noise or
surrounding reverberation alongwiththevoicesignal which
is unwanted and shouldn’t process further.
2.2 Voice Pre-processing
In this step pre-processing block plays an important role in
eliminating the unwanted signal. It finally improves the
accuracy of voice recognition. The voice pre-processing
normally includes filtering of noise, smoothing of signal,
point to point detection, framing of signal, windowing of
signal, & cancelling and removing of echo. It processed only
original data further.

Fig. 1 Block Diagram of Voice Recognition Based
Automation System
2.3 Feature Extraction
As we know that the voice differs from person-to-person.
This is due to the fact that every person has different vocal
cord which sounds differ. Theoretically, it is possible to
recognize voice from the digitized waveform. But due to the
large variation in voice signal, it is necessary to perform
some feature extraction to reduce that variations and
unwanted signal. There are different technologies are
available for feature extraction, which are as follow. These
technologies are also useful in other areas of voice
processing
1. MFCC –Mel Frequency Cepstrum Coefficients (MFCC) is
the most important method used in the process of feature
extraction in voice recognition. As it has frequency domain
features, therefore it’s more accurate than time domain
features .It represents the original cepstral of windowed
short time signal which is expressed from Fast Fourier
Transform (FFT). These coefficients are robust and
dependable for the variations of speaker and the
environment where operation is performed.
2. LPC –Linear Predictive Coding (LPC) is a tool which is
widely used for medium or low bit rate coder. In this
method digital signal is compressed for propertransmission
and storage. Computation of parametric model based on
least mean squared error theory is known as linear
prediction (LP).
2.4 Feature Classification
The most common techniques which are used for feature
classification are discussed below. This type of system has
complex mathematical functions and they take out hidden
information from the input signal.
HMM – Hidden Markov Model. (HMM) is the mostly used
pattern recognition technique for voice recognition. HMM is
a mathematical model which is signalized on the Markov
Model and has a set of output distribution. In HMM method,
voice is break down into smaller audible parts and these
parts represent the state in the Hidden Markov Model. And
according to the probabilities of transmission, there is a
transmission from one state to another.
DTW –Dynamic Time Warping (DTW) technique compares
the words with stored words. In this method, the time
dimensions of the unknown words are changed until and
unless they match with that of the stored word.
VQ –Vector Quantization (VQ) is a method in which the
mapping of vector is done from a large vector space in a
specific number of regions in that space. Each region is
known as cluster and it can be shown by its centre which is
known as a code-word.
Voice Acquisition
Voice Pre-
Processing
Feature
Extraction
Feature Classifier
Voice
Recognition
Arduino
Controller
Relay
Driver
Circuit
LED
Buzzer
Bed Elevation
Motor to
Represent
Wheel Chair

3. ARTIFICIAL NEURAL NETWORK FROM THE
VIEWPOINT OF VOICE RECOGNITION
3.1 What is Artificial Neural Network?
Artificial Neural Networks (ANN) is nothing but the
structured electronic model which is based on neural
structure of our brain. The human brain simply learns from
the experiences. In the same way ANN trained the data and
stored it for the next process.
ANN is a computer model which hasthesamearchitecture of
human brain. They generally involve hundreds of quite
simple processing units which are wired together in a
complex communication network. Each simple processing
unit actually represents a real neuronwhichtransmitsa new
signal if it receives a strong signal from otherconnectedunit.
3.2 Artificial Neuron
Artificial Neurons are the basic unit of Artificial Neural
Network which reproduces the four basic function of
biological neuron performed by human being brain. It is a
mathematical function which is based on model of natural
neuron of human brain. The given below figure shows the
basic artificial neuron.
Fig. 2: Basic Artificial Neuron
In The above figure, various inputs are shown by the
mathematical symbol, i.e. i(n). Each inputs are multiplied by
respected connecting weights w(n).
Generally, these products are simply added and given to the
transfer function to generate the desired results. The
applications for example, text recognition and voice
recognition are required to convert these real word inputs
into discrete values. In the software system, these neurons
are called as processing elements and it has many more
capabilities than the basic artificial neuron which has
described above.
4. SOFTWARE IMPLEMENTATION
To design the system we have written code on MATLAB.
MATLAB is a fourth-generation high-level programming
language and it provides an interactive environment for
numerical computation, visualization and programming.
It provides matrix manipulations; plotting of functions and
data; implementation of different algorithms; creation of
user interfaces; it provides interfacing with programs which
is written in other languages, including C, C++, Java, and
FORTRAN. It analyzes the data; it develops the algorithms;
and creates required models and applications.
It has numerous inbuilt commands and mathematical
functions that help user in mathematical calculations, and
generating plots, it is also used for performing numerical
methods.
Fig 3. A Graph of Mel Frequency Cepstrum.
5. HARDWARE IMPLIMENTATION
The hardware implementation of proposed system is
explained below.
5.1 Microphone and Voice Recognition Module
The microphone which is used to acquire voice signal and
sends it to the voice recognition model is basically a collar
type microphone with 3.5 mm jack. In this system we have
used Elechouse voice recognition module v3 for the voice
recognition process which has shown below in the Fig. The
voice recognition module should be trained first and then it
can be used to actually recognize the voice commands by
speaker.
The voice input from the microphone is fed to the voice
recognition model and here theinputvoiceiscompared with
the trained voice commands which are stored previously,
and if it is matched with stored and traineddata thencontrol
action will take place in control circuit.Thevoicerecognition
model v3 can actually store up to 80 commands of 1400 to
1500ms each in its library and out of 80 commands only 7
commands can be used in recognizer for the process of
recognition. Thus at a time only 7 commands are active and
to add next 7 commands, it is necessary to clear the
recognizer first.
The selected model has two ways of controlling the Serial
Port, General Input Pins and General Output Pins. It has a
recognition accuracy of 99% under suitable conditions.

Fig. 4.Voice Recognition Module v3
5.2 Arduino Uno Microcontroller
The Microcontroller we are using for the proposed model is
shown below in Fig. 5.TheArduinomicrocontrollerprovides
an inexpensive, cheap, platform for students and
professionals to create the devices that interact with
respected environment using different types of sensors and
actuators.
Arduino microcontroller has integrated development
environment (IDE) which easily runs on a PC and it allows
user to write programs for microcontroller in C or C++
language, which is easy and robust language compare to
other language. The Arduino microcontrollerboardbased on
the AT mega 328. Following are the features of Arduino
microcontroller. It has 14 digital input/output pins (Out of
these 14 pins the 6 can be used as PWM outputs) and 6 is
used for analog inputs.
It operates on 5V D.C and it has a clock speed of 16 MHz
It has Input Voltage (recommended) 7-12V
It has DC Current per I/O Pin 40 mA
It has flash Memory of 32 KB of which 0.5 KB used by
Boot loader
Fig. 5 Arduino Uno Microcontroller
5.3Buzzer
In this model we are using buzzer as an indicator through
which the respective person of the patient will come to the
patient and check him, whenever buzzer makes sound. Ifthe
patient needs any help then by voice command he orshe can
turn on the buzzer for help.
A buzzer or beeper is an audio signaling device, which may
be mechanical, electromechanical, or piezoelectric.
Fig. 6 5V Buzzer Module And Schematic Diagram.
5.3.1Testing Results.
Below we have shown the testing result in which the output
is the turning on and off of the buzzer every alternate
second. The picture below shows the setup of our module
and Arduino.
Fig.7 Showing Testing Result
5.4 Relay Circuit
To control the appliances generally relays are used with the
Arduino microcontroller .The relays which we have used in the
system are 5V-5 pin relay which is shown below in Fig. 8
Normally it remains in closed state. When the relay coils are
energized the relay switches itself from normally closed state to
normally open state due to the electromagnetic induction The
normally open state (N.O) of relays are used in the automation
system.

Fig.8 2 Channel 5V 10A Relay Model And Schematic
Diagram
5.5 12V, 5A Power Supply
All electronic circuits work only on lowdc voltage.Therefore
It needs a power supply unit which can provide the desired
voltage supply. The power supply which is required for this
automation system are 5V for the relay circuit module and
12V 5A for the motor for bed elevation.
The requirement of 5V can be fulfilled from Arduino board
itself, but for the 12V 5A supply we need additional power
supply circuit. A Centre tap transformer of 15-0-15 V is used
in this power supply. After that we use a bridge rectifier
circuit which is used to converts the A.C to D.C.
The D.C which we get after conversion is not ripple free,
therefore two capacitor C1of 3300μF and C2=0.33 μF are
used to remove ripple. To regulate the voltage for power
supply LM338K Voltage regulator is used which gives
regulated voltage of 12V and constant current of 5A.
The capacitor C3 of 100μF is usedtoremovetheripplesfrom
the output voltage and the diode D3 is used to protect the
circuit when the capacitor C3 starts discharging. Fig. 9 given
below shows the circuit diagram of the 12V, 5A power
supply.
Fig.9 Circuit Diagram of Power Supply
6. DC MOTOR.
In this model we are using motor for bed elevation. We are
using two motor which will elevate or down the bed
according to the command. When the motor will run anti
clockwise it will elevate the bed and whenthemotor will run
clockwise the bed will be lowered. The motor which we are
using here has following some feature. It has 12.0VDC,it has
output speed of 200+ rotation per minute. The rotation
output is CW/CCW. It is resistant to noise.
7. CONCLUSIONS
ANN is one of the most reliable techniques for the future
computation. The model shows that it can be very useful in
voice signal classification.Itfunctionsmorelikehumanbrain
than conventional computer logic.
ANN has better voice recognition rates than MFC, but it is
complex to train algorithm and it is dynamically sensitive,
which may cause problems. The future of this technology is
very great and the only thing which has to improve is
hardware development as ANN need faster hardware.
The voice recognition based automation system has built
and implemented. The system is specificallydesignedfor the
patient suffering from paralysis and alsofortheaged people.
A wooden adjustable bed fitted with motor is which is very
economic and affordable.
The adjustable bed offers two elevation positions sleep
position, and sitting position and according to patient’s
comfort he or she may choose the desired positionbysimply
saying it, which will act as voice commands for the system.
The use of voice commands removes the necessityofremote
controls and other electronic device and makes it simple to
interact with the system to perform the function andcontrol
the devices.
Buzzer allows patient to notify the guardians whenever the
patient is in need of help. The LED can be used for different
purpose; it can be used to indicate multiple requirementsby
patients.

8. REFERENCES
[1].T.Kirankumar and B. Bhavani, “A Sustainable Automated
System for Elderly People Using Voice Recognition and
Touch Screen Technology,” International Journal of Science
and Research (IJSR), vol. 2, pp. 265-267, August 2013.
[2]. Rajesh Khanna Megalingam, Ramesh Nammily Nair, and
Sai Manoj Prakhya, “Automated Voice based Home
Navigation System for the Elderly and the Physically
Challenged,” in International Conference on Advanced
Communication Technology, Seoul, pp. 603-608, February
2011.
[3]. Arthi.J.E and M.Jagadeeswari, “Control of Electrical
Appliances through Voice Commands,” IOSR Journal of
Electrical and Electronics Engineering, vol. 9, pp. 13-18,
February 2014.
[4].Parameshachari B D, Sawan KumarGopy,Gooneshwaree
Hurry and Tulsirai T. Gopaul., “A Study on Smart Home
Control System through Speech,” International Journal of
Computer Applications, vol. 69,pp. 30-39, May 2013.
[5]. Norhafizah bt Aripin and M. B. Othman, “VoiceControl of
Home Appliances using Android,” in International
Conference on Electric Power, Electronic, Communication,
Control, And Informatic Systems, Malang ,pp. 142-146,
August 2014.
[6] .S. M. Anamul Haque, S. M. Kamruzzaman and Md.
Ashraful Islam1, “A System for Smart-Home Control of
Appliances Based on Timer and Speech Interaction,” in
Proceedings of the 4th International Conference on
9. BIOGRAPHIES
Sanu Kumar Das is a student of Dr.D.Y.Patil
College of Engineering, currently pursuing
his B.E in Electronics & Telecommunication.
His areas of interest are DSP, Signal &
System, and Digital Communication System.
Akhilesh Yadav. B is a student of Dr.D.Y.Patil
His areas of interest are Digital Control
System, Embedded Systems etc.
Vitthal Rathod is a student of Dr.D.Y.Patil
His areas of interest are Power Electronics
and Drives, Digital Control, ANN & Fuzzy
Logic Applications etc.

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