IRJET- Tara - An Accident and Alcohol Detecting Bluetooth Enabled Smart Helmet

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 03 | Mar 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2043
Tara - An Accident and Alcohol Detecting Bluetooth Enabled
Smart Helmet
Ranjith Kumar S1, Medikurthi Sai Deshik2, Ragul K3, Assistant. Prof. R Yamini M.E.4
1,2,3Student, Dept. of Computer Science and Engineering, Adhiyamaan College of Engineering, Hosur
4Assistant Professor, Dept. of Computer Science and Engineering, Adhiyamaan College of Engineering, Hosur
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Abstract - With the growing number of 2-wheel motor
vehicles, frequency of accidents is on the rise. A major
portion of the fatalities occur because the person was
either not wearing a helmet, or his accident was not
reported in time, and he could not be saved because of the
delayed admittance to a hospital, or because he was riding
while drunk. We propose mechanisms that can detect if
one is wearing the helmet, detect accidents, and detect
whether the person has over-consumed alcohol. For this
purpose, we use on board sensors – touch sensor, alcohol
sensor (MQ-135), MEMS sensor and breath-analyser
(MQ6). The MEMS sensor measures the change in tilt, in X
Y and Z axes respectively, and sends the alert message to
the pre-set contacts. The breath analyser senses the breath
of a person wearing the helmet and confirms the presence
of a human in the helmet. The moisture sensor clears the
windscreen during fog and rain making riding
comfortable. This can help optimize accident detection in
the future when enough data is gathered to provide
reliable accuracy. The helmet can connect to any
smartphone via Bluetooth, to communicate with the online
API, using the internet connection of the smartphone. This
will ensure the holistic safety of the rider at all times.
Keywords: Internet of Things, Smart Helmet, Rain
Detection, Accident detection, Alcohol detection
I. INTRODUCTION
Motorcycles and bikes form an integral part of
personalized transportation in India. However,
unfortunately, it also involves innumerable accidents
and subsequent loss of lives. Every year, about 300,000
teenagers go to the emergency department because of
bike injuries, and at least 10,000 teenagers have injuries
that require a few days in the hospital. Statistics say,
motorcycle deaths accounted for 15 % of all motor
vehicle crash deaths in 2015 and were more than double
the number of motorcyclist deaths in 1997. Through an
ONEISS survey conducted by the Department of Health,
it was found that 90% of the motorcycles rider killed in
accidents were not wearing a helmet at the time of
impact. This, along with drunken driving are a major
reason of accidents. We aim to mitigate these problems
and hence the associated casualties by ensuring that the
rider will wear the helmet all the time during his/her
ride, thus ensuring safety. The helmet can understand if
the person is wearing the helmet, using the pressure
sensors, fitted inside the padding foam. The helmet can
detect a possible accident, using the on board
accelerometer and pressure sensor. If the values
detected exceed a threshold, it is reported as an accident.
Emergency contacts, specified by the rider during app
setup, are informed about the possible accident, via a
system generated email and text message, containing the
address and GPS coordinates where the accident had
been detected. An on board alcohol sensor also analyses
the breath of the rider to detect if the current
intoxication level is above the legal threshold. If he rides
it anyway, his emergency contacts are informed, so that
they may handle the situation. The helmet can connect to
any smartphone via Bluetooth so that it can
communicate with the server using the smartphone’s
internet access.
1.1 Methodology
TTP223 Sensor are mainly used for “touch sensitive”
application. It is also used for detecting physical
pressure, squeezing and weight. Here we use TTP223
Sensor because of simple of use and low cost. It has 2
pins in it. It changes its resistive value on pressing hence
more the one presses its resistance go down. It is placed
inside the helmet above the position of head. An alcohol
sensor is suitable to check whether the driver is drunk or
not, hence it is placed in the helmet which covers the jaw
region of the rider so that it placed right in front of
rider’s mouth. As per the section 185 of the Motor
Vehicle Act, the blood alcohol content (BAC) legal limit is
30mg alcohol in 100ml blood. Here for demonstration
purpose we program the limit as 350mg/L. Since it is
highly sensitive to alcohol vapour and less sensitive to
benzene it is used to check the alcohol consumed by the
driver and its sensitivity can be adjusted by using the
potentiometer. It consists of 5 pins. Hence wearing the
helmet is confirmed using TTP223 sensor and breathe
analyzer. It detects the alcohol within the breath and
sends the signal to the controller. The rain sensor is used
to detect the presence of moisture on the windshield of
the helmet and initiate the controller to turn on the

wiper to wash away the presence of moisture at the
earliest to make riding two wheelers during rain easier.
The MEMS sensor is used to detect the shock if any in the
helmet in case of a sudden tilt towards either of the
directions of the helmet. The MEMS sensor intimates to
the controller to send a message to the pre-set contacts
via the SIM 800C GSM Module. The emergency message
will contain the location of the unusual tilt in the helmet.
The location is retrieved using the NEO 6M GPS module.
The ignition in the vehicle is allowed only if the human is
detected by the helmet. The ignition is not allowed if the
alcohol is detected by the alcohol sensor. A BLE HM-10
bluetooth module is used to check the health of the
individual components of the device. An LCD display is
attached to the device which constantly keeps updating
us of the status of the functioning of individual
components of the device. The monitor readings would
consist of readings like left emergency and right
emergency from the MEMS sensor, motor running speed
from the controller. The device can be paired with any
mobile using the bluetooth module. Through this the
device and the components can be controlled using the
mobile phone.
1.2 Block diagram
Receiver Unit
Transmitter Unit
2. RELATED WORKS
In the literature, we found several smart helmet system
but with different approach and proposed solution.
Wilhelm Von Rosenberg et al [1] has proposed a
smart helmet with embedded sensors for cycling and
Moto racing to monitor both vital signs and the
electroencephalogram (EEG) simultaneously. They have
embedded multiple electrode within a standard helmet
and a respiration belt around the thorax for validation
and a reference ECG from the chest. Also a multivariate
R-peak detection algorithm has been applied to get data
from real life noisy environment.
Sreenithy Chandranet al [2] monitored the value
received from accelerometer embedded in helmet and
detects an accident by analysing those values and sends
an emergency notification to contacts with Global
positioning system location.
C. J. Behr et al [3] has developed a smart helmet
for the mines industry keeping focus air quality, helmet
removal and collision. It detects the presence of
hazardous gases like CO, SO2, NO2 and detects the
helmet removal using an off-the shelf IR sensor. An
accelerometer is implemented to detect accident by
calculating the dangle of the helmet and shock detection
by software using those reading.
Sudhir Rao Rupanagudi et al [4] has made a
helmet which monitors the real life traffic scenario
behind the motorcycle rider and a intimation system to
inform him / her. A MATLAB based algorithm is
implemented to perform the task along with a cost
effective setup and priority has been given to some
special cases such as turning and all.
A. Ajay et al [5] has proposed a smart helmet
with 4 different functionality : accident identification and
alert system, voice based navigation system using map, a
bluetooth device based voice call which uses voice
recognition to attend cellular call and a solar panel for
external power source. A GSM module attached arduino
board is used for accident alert system and a smart
mobile phone is interfaced with the helmet to serve the
navigation process.
Mohd Khairul Afiq Mohd Rasli et al [6] has
designed a PIC16F84a microcontroller controlled smart
helmet with force Sensing Resistor (FSR) to detect
rider’s head and a BLDC Fan for speed detection. Helmet
starts it’s alarm system whenever the speed crosses a
certain limit and motorcycle’s engine will start only after
the rider buckles the helmet.
Muthiah M et al [7] proposed an helmet which
contains a automatic safety headlight that reacts
according to rider’s facial movement with the help of
accelerometer and other sensors and motors.
3. MATERIALS AND METHODS
3.1 Hardware Components
Table 1. List of Hardware
COMPONENTS SPECIFICATIONS
Microprocessor PIC 16F877A
Alcohol Sensor MQ 135

Breath Sensor MQ 6
Accelerometer MEMS Sensor
Bluetooth BLE HM-10
Rain Sensor Rain Sensor Module
Touch Sensor TTP223 Module
GSM SIM 800C GSM Module
GPS NEO 6M GPS Module
Motors 100 RPM and 10 RPM
Motors
3.1.1 Microcontroller (PIC 16F877A)
PIC 16F877A is the core of the device. It is one of the
most renowned microcontrollers in the industry. This
microcontroller is very convenient to use, the coding or
programming of this controller is also easier. One of the
main advantages is that it can be write-erase as many
times as possible because it uses FLASH memory
technology. It has a total number of 40 pins and there are
33 pins for input and output.
Fig 1. PIC16F877A
When the device is started for the first time, the
application prompts to calibrate the helmet. Upon
calibrating, the calibrated values of the accelerometer
are stored in the microcontroller’s ROM. This is used to
calculate the ‘tilt’ of the helmet while riding the
motorbike.
3.1.2 Alcohol Sensor (MQ 135)
This module uses an Alcohol Gas sensor MQ 135. It is a
low cost semiconductor sensor and can detect the
presence of alcohol gases at concentrations from 0.05
mg/L to 10 mg/L. The sensitive material used in this
sensor is SnO2, whose conductivity is lower in clean air.
Its conductivity increases as the concentration of alcohol
gases increases. It has high sensitivity to alcohol and has
a good resistance to disturbances due to smoke, vapour,
and gasoline. This module provides both digital and
analog outputs. MQ 135 alcohol sensor module can be
easily interfaced with microcontrollers.
Fig 2. MQ 135 Alcohol Sensor
3.1.3 Breath Analyzer (MQ 6)
The MQ-6 module is used in gas leakage detecting
equipment in family and industry. This module has high
sensitivity to LPG, iso-butane, propane and LNG. It can
also be used to detect the presence of alcohol, cooking
fumes, and cigarette smoke. The module gives out the
concentration of the gases as a analog voltage equivalent
to the concentration of the gases. The module also has an
on board comparator for comparing against an
adjustable pre-set value and giving out a digital high or
low. This sensor also detects the presence of CO2 gas in
the exhaled air. Thus it can also be used as breath
analyser. It functions as a human detector which
confirms the presence of human within the helmet
thereby allowing the controller to ignite the vehicle.
Fig 3. MQ 6 Breath analyzer
3.1.4. Accelerometer (MEMS Sensor)
The accelerometer we use is a 3-axis accelerometer that
measures tilt of the device with respect to the earth, in 3
axes X, Y and Z. Since it measures the tilt with respect to
the earth, we need to store the corresponding values
when the helmet it normally worn. This is the calibration
we require. The values are stored in the ROM, and the
difference of current readings and the stored readings
are used to detect a possible accident.
Fig 4. MEMS Sensor
3.1.5 Bluetooth (BLE HM-10)
The HM-10 is a Bluetooth 4.0 Low Energy module
containing the TI produced CC2540 or CC2541.It
provides reliable and low power consuming Bluetooth
connectivity. We use it for the communication between
the device and the smartphone, and thus it is our
medium of data communication. Another important

reason why we use this particular Bluetooth module is
that it is power efficient and low on cost.
3.1.6 Rain Sensor Module
A rain sensor or rain switch is a switching device
activated by rainfall. There are two main applications for
rain sensors. The first is a water conservation device
connected to an automatic irrigation system that causes
the system to shut down in the event of rainfall. The
second is a device used to protect the interior of an
automobile from rain and to support the automatic mode
of windscreen wipers. An additional application in
professional satellite communications antennas is to
trigger a rain blower on the aperture of the antenna feed,
to remove water droplets from the mylar cover that
keeps pressurized and dry air inside the wave-guides.
Fig 5. Rain Sensor
3.1.7 Touch Sensor (TTP223 Sensor)
Touch Sensors are the electronic sensors that can detect
touch. They operate as a switch when touched. These
sensors are used in lamps, touch screens of the mobile,
etc… Touch sensors offer an intuitive user interface.
Touch sensors are also known as Tactile sensors. These
are simple to design, low cost and are produced in large
scale. With the advance in technology, these sensors are
rapidly replacing the mechanical switches. Based on
their functions there are two types of touch sensors-
Capacitive sensor and Resistive sensor.
Fig 6. TTP223 Touch Sensor
3.1.8 GSM (SIM 880C)
SIM 800C Module is a complete Quad-band GSM/GPRS
solution in a SMT type, which can be embedded in the
customer applications. These modules are sub-system of
the Internet-of-everything hardware. SIM800C supports
Quad-band 850/900/1800/1900MHz, it can transmit
Voice, SMS and data information with low power
consumption. With tiny size of 17.6*15.7*2.3mm, it can
smoothly fit into slim and compact demands of customer
design.
Fig 7. GSM SIM 800C Module
3.1.9 GPS (NEO 6M )
GPS receiver module gives output in standard (National
Marine Electronics Association) NMEA string format. It
provides output serially on TX pin with default 9600
Baud rate. This NMEA string output from GPS receiver
contains different parameters separated by commas like
longitude, latitude, altitude, time etc. Each string starts
with ‘$’ and ends with carriage return/line feed
sequence.
Fig 8. NEO 6M GPS Module
3.1.9 Motors (100 RPM and 10 RPM Motors)
A DC motor is any of a class of rotary electrical motors
that converts direct current electrical energy into
mechanical energy. The most common types rely on the
forces produced by magnetic fields. Nearly all types of
DC motors have some internal mechanism, either
electromechanical or electronic, to periodically change
the direction of current in part of the motor.
Fig 9. 100 RPM Motor Fig 10. 10 RPM Motor
4. RESULT
The prototype was checked completely to the extremes
and it was found that the ignition was allowed only after
the satisfaction of the three main conditions. The device
detects the human, alcohol and accident. The wiper of
the vehicle functions well immediately at the point of
detection of water droplets. The Bluetooth module helps
in checking and maintenance of the health of individual
components of the device and the device as a whole.

Smart helmet
Location Retrieved
5. CONCLUSIONS
The prototype developed yielded satisfactory
results. The accuracy and precision are high, which
shows that our proposed mechanism is accurate in
detecting an accident, a human and high alcohol
consumption.
However, during accident detection, there have
been many cases, where the alarm has been rung. The
comparison of the parameters for accident detection,
with and without the use of the alarm, shows how
important the use of an alarm is, to report false accident
detection. However, repeated unwanted need to respond
to the alarm while driving can cause discomfort and
distraction.
6. REFERENCES
[1] Saha, Himadri Nath, Abhilasha Mandal, and Abhirup
Sinha. "Recent trends in the Internet of Things."
Computing and Communication Workshop and
Conference (CCWC), 2017 IEEE 7th Annual. IEEE, 2017.
[2] Wilhelm Von Rosenberg, Theerasak
Chanwimalueang, Valentin Goverdovsky, David Looney,
David Sharp, Danilo P. Mandic,Smart Helmet: Wearable
Multichannel ECG and EEG, IEEE Journal of Translational
Engineering in Health and Medicine.
[3] Sreenithy Chandran ; Sneha Chandrasekar ; N Edna
Elizabeth, Konnect: An Internet of Things(IoT) based
smart helmet for accident detection and notification,
India Conference (INDICON), 2016 IEEE Annual
[4] C. J. Behr; A. Kumar; G. P. Hancke , A smart helmet for
air quality and hazardous event detection for the mining
industry, 2016 IEEE International Conference on
Industrial Technology (ICIT)
[5] Sudhir Rao Rupanagudi ; Sumukha Bharadwaj ;
Varsha G. Bhat ; S. Eshwari ; S.Shreyas; B. S. Aparna ;
Anirudh Venkatesan, Amrit Shandilya, Vikram
Subrahmanya, Fathima Jabeen A novel video processing
based smart helmet for rear vehicle intimation &
collision avoidance, 2015 International Conference on
Computing and Network Communications (CoCoNet)
[6] G.Vishnu; V. Kishoreswaminathan; V.Vishwanth ;K.
Srinivasan ;S. Jeevanantham, Accidental identification
and navigation system in helmet, 2017 International
Conference on Nextgen Electronic Technologies: Silicon
to Software (ICNETS2)
[7] Mohd Khairul Afiq Mohd Rasli ; Nina Korlina Madzhi ;
Juliana Johari, Smart helmet with sensors for accident
prevention, 2013 International Conference on Electrical,
Electronics and System Engineering (ICEESE)
[8] Muthiah M ; Aswin Natesh V ; Sathiendran R K, Smart
helmets for automatic control of headlamps,
International Conference on Smart Sensors and Systems
(IC-SSS)

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