As the number of motorcycles have been increasing in the country day-by-day, there is an increased possibility of road accidents to a greater extent. The major reasons in road accidents are drunken driving and driving without helmet, although the latter is compulsory. Government is also working on this, and tries to insist people to use helmet while driving. For encouraging people to wear Helmet, industries try to add smart devices to the helmet, those devices contribute to the smart helmet. It is necessary to develop an embedded system to perform few dedicated functions in order to ensure the safety of the rider. Microprocessors and Microcontrollers are widely used in embedded system design products. An embedded product is controlled by its own internal microprocessor or microcontroller as opposed to an external oscillator. The key characteristic, however, is being dedicated to handle a particular task. There are several Smart Helmet Systems that were developed by previous researchers, but those systems have not supported convenience and low cost. So it is necessary to increase the reliability of the system by reducing the size and cost of the product.

Bayly et al. (2006) have proposed an Intelligent Transport Systems and Motorcycle Safety. They investigated the extent to which ITS have been applied to motorcycles (including both existing and emerging technologies) and have discussed these ITS according to their likely safety benefits to motorcycle safety. The potential to adapt emerging and existing ITS from other vehicles to motorcycles is also highlighted in their experiment.

Mohamad et al. (2013) have developed a Vehicle Accident Prevention System Embedded with Alcohol Detector. Alcohol in the blood or Blood Alcohol Content (BAC) is usually expressed as a High BAC of the drunken driver that affects their behaviour like unconsciousness, emotional swing, and anger or sadness. The vehicle accident prevention system can be one of the solutions to avoid drunken driving as it could detect the BAC through human breath using alcohol sensor. This system will evolve alarm as warning and the driver would be unable to start the car engine if the alcohol sensor detects a certain value of BAC.

Sudharsana Vijayan et al. (2014) have proposed an Alcohol Detection using Smart Helmet System. A Smart Helmet automatically checks whether the person is wearing the helmet and has non-alcoholic breath before driving. This work consists a transmitter at the helmet and a receiver at the bike. There is a switch to make sure the helmet is worn. The ON condition of the switch ensures the placing of the helmet is in proper manner. An alcohol sensor is placed near to the mouth of the driver in the helmet to detect the presence of alcohol. The engine is controlled through a relay and a relay interfacing circuit.

Manikandan et al. (2016) have proposed a Helmet using GSM and GPS Technology for Accident Detection and Reporting System. This research work has fulfilled the purpose of saving lives during collision of a vehicle. Another feature of the proposed system was the ability to detect an accident and send the corresponding geographical coordinates of the accident spot to the predefined registered numbers using a GPS and GSM system, respectively.

Gupta et al. (2016) have done a research work on Alcohol Detection with Vehicle Controlling to make vehicle driving more safer. The vehicle ignition system would turn OFF on detecting the presence of alcohol using an alcohol detector connected to the Printed Circuit Board (PCB) when the level of alcohol crosses a permissible limit.

Limbanee et al. (2016) have proposed a Smart Helmet to Avoid Road Kills (SHARK) using a WSN based solution, which warns the driver to wear a helmet while riding the two wheeler. The system is comparatively cheaper than conventional systems.

Rasli et al. (2013) have proposed a Smart Helmet with sensors for accident prevention. For better safety of the system, a speed alert is given to the motorcyclists.

Agarwal et al. (2015) have implemented a Smart-Helmet System with GSM technology to ensure if the person wears a helmet and has a non-alcoholic breath before driving. If any of these conditions are not met, the bike does not start and a message is sent to the concerned person. In case alcohol is detected, the GSM module attached to the receiving unit sends message to the registered mobile number.

Kiruthika et al. (2016) have proposed an Intelligent Life Saving Helmet Detecting System using MSP430. The main purpose of the paper is to encourage wearing helmet. Unless the rider is wearing a helmet and has not consumed alcohol, the system does not allow ignition of the engine.

Kumar et al. (2015) have implemented a Solar Powered Smart Helmet with multifeatures. The helmet is equipped with cooling device modules, which will reduce the temperature inside the smart helmet by using solar energy.

Hobby et al. (2016) have developed a device for a smart helmet that includes a camera, communication subsystem, and control subsystem. The control subsystem processes the video data from the camera and the communication subsystem transmits this video data from the smart helmet to a destination device.

Jadhawar (2016) has proposed a system that consists of microcontroller along with IR and PIR sensors for helmet authentication followed by alcohol detection using MQ3 sensor. Secondly, MQ3 sensor detects the presence of alcohol. The engine would not start if the biker has consumed alcohol. It also detects fall detection through an accelerometer. This system is very much helpful in preventing fatal accidents.

Kulkarni and Sangam (2017) have developed a smart system that detects and evaluates air quality by finding out toxic and hazardous gases found in the underground mining industry. The system detects harmful gases, such as Methane, Propane, Butane, Benzene, Carbon monoxide, and so on. An ARM processor and a ZigBee transmitting unit are positioned in the helmet of the person working in the underground mines. This system warns all miners who are working inside the mine.

Magno et al. (2016) have designed a wearable system to improve motorcycle safety by transforming a helmet into a smart, multi-sensor connected helmet (SHelmet). The system is equipped with a dense sensor network, including accelerometer, temperature, light, and alcohol gas level, and a Bluetooth low energy module. Self-sustainability has been achieved by this system and the functionality of the developed node was obtained.

von Rosenberg et al. (2015) have proposed an integrated system, which is to be incorporated inside an ordinary motorcycle helmet. The vital signs and brain activities can be readily recorded from the helmet in a more comfortable way.

Bisio et al. (2017) have implemented a system to recognize and detect a brain stroke with an accuracy of 90% in case of an accident before the medical team arrives. This is an NN-based SH system. A MultiLayer Perceptron (MLP) model was employed that implements a 4-layer NN.

Behr et al. (2016) have also developed a smart helmet system, which would be able to detect a hazardous situation in the mining industry. They have considered three main specifications during their implementation, namely air quality, helmet removal, and collision (miners are struck by an object). The whole software implementation was done based on Contiki Operating System in order to do the control of the measuring of sensors and of calculations done with the measured values.

Hartwell and Brug (2004) have introduced a smart helmet with integrated electronics providing safety and convenience features, which is aware of the user's location and interactions with the environment. The helmet features, include a global locating system, an environmental interaction sensor, a mobile communications network device, a small display panel, a microphone, and at least one speaker. This system was implemented with a good efficiency.

(1999) have developed a smart low-profile helmet-mounted antenna with pattern diversity. The researchers believe that they have implemented the first practical portable smart antenna systems. >

Nasr et al. (2016) have conveyed a smart and reliable IoT system solution, which instantly notifies whenever an accident occurs and locates its exact geographic coordinates on the map. When an accident takes place, a shock sensor installed in the system would detect it. The geographical data collected from this system could be relied upon as an admissible evidence or an indicator of the road’s state and conditions.

Momin et al. (2017) have introduced a system that consists of sensors like limit switch sensor, alcohol sensor, and accelerometer sensors. A limit switch sensor is used to check whether a helmet is worn or not. The alcohol sensor is used to check alcohol presence in the driver's breath. If a rider is wearing the helmet and not drunken, then fuel and ignition system begins its function. Accelerometer sensor is utilized to measure the tilt position of the helmet. If an accident has occurred, accident location could be traced by using the GPS module and then a message is sent to the family member’s registered contact number and also to the nearest hospital by using the GSM module.

The authors have developed a Smart Helmet System for reducing the number of road accidents that happen due to drunken driving and failing to wear a helmet. The proposed system detects the presence of alcohol and locks the engine immediately, for which an MQ-3 alcohol sensor is used. An FSR sensor has been also used to detect the absence of a helmet, which then sends the signal to the receiver making the vehicle's engine not to function. Therefore this system supports wearing a helmet and tries to reduce fatal accidents in future. This application is in the area of embedded systems. An embedded system is the combination of computer hardware and software, either fixed in capability or programmable, that is specifically designed for a particular function.

In this research work, the authors use Arduino microcontroller to control the entire system. There are two Arduino microcontrollers used, one in helmet unit and other in bike unit, where both have different tasks. In bike unit, the sensors detect the presence of alcohol and the helmet unit sends the signal with the help of the transmitter connected with Arduino in the helmet. The receiver connected with the Arduino in the bike unit receives the signal from the receiver and reacts as per the received signal.

By using an Arduino microcontroller, the manufacturing cost can be reduced, and the program can be done through Arduino IDE platform. It is simple to develop a program through this platform. Together with the sensors the helmet is called as smart helmet, by using this smart helmet accidents could be reduced.

The Arduino Uno is a microcontroller board based on the ATmega328 (datasheet). It has 14 digital input/output pins (of which 6 can be used as PWM outputs), 6 analog inputs, a 16 MHz crystal oscillator, a USB connection, a power jack, an ICSP header, and a reset button. It is simply connected to a computer with a USB cable or a AC-to-DC adapter or battery can be used to power it to get started (Arduino Uno REV3 Overview, n.d.). An Arduino microcontroller is shown in Figure 1.

Figure 1. Arduino

A simple motor is used here to resemble the bike engine. Motorcycle engines are typically two-stroke or four-stroke internal combustion engines, but other engine types (such as Wankels and electric motors) have been used in small numbers.

The 16 x 2 LCD board makes it easy to interface a module with low cost microcontroller development board, which do not have built-in support for LCD modules, but has the contrast adjust resistor and Backlight current limiting resistor. The LCD with microcontroller is shown in Figure 2.

Figure 2. LCD with microcontroller

The alcohol sensor used in this work is MQ-3 Sensor, which is used to detect the presence of alcohol content in human breath. It is suitable for detecting Alcohol, Benzene, CH , Hexane, LPG, CO, etc. Due to its high 4 sensitivity and fast response time; measurements can be taken as soon as possible. The sensitivity of the sensor can be adjusted by using a potentiometer. Figure 3 shows the MQ-3 Sensor used in this experiment.

Figure 3. MQ-3 Sensor

When external force is applied to the sensor, the resistive element is deformed against the substrate. Air from the space opening is pushed through the air vent in the tail and the conductive material on the substrate comes into contact with parts of the active area. The FSR's output signal is a monotonic function of area and pressure. When enough force is applied, this function changes the slope quickly due to sensor saturation (Joe, n.d.). Figure 4 shows the FSR Sensor.

Figure 4. FSR Sensor

An RF module (Radio Frequency module) is a (usually) small electronic device used to transmit and/or receive radio signals between two devices. In an embedded system it is often desirable to communicate with another device wirelessly using this device, which is shown in Figure 5.

Figure 5. RF module

The flowchart of the proposed system is shown in Figure 6. In the proposed system there are two main steps for considering safety, the first step is to identify whether the person wears a helmet or not, for which the authors utilize the FSR sensor. The circuit designed in the work begins its function for engine ignition only if the person wears a helmet. The second step is alcohol detection. If the rider's breath exceeds certain limit, then the alcohol sensor detects for it and halts the engine ignition. This can be done using MQ-3 sensor. The engine operates only when both conditions are satisfied. Any normal helmet can be connected to any bike with this wireless technology. Also, the authors consider product cost as an important constraint, so that it can be affordable to a common person.

Figure 6. Flow of the Proposed System (a) Helmet Unit (b) Bike Unit

The FSR sensor is placed on top of the helmet and the MQ3 sensor is placed above the front portion of the helmet. The FSR sensor senses whether the person has wore a helmet or not and the MQ3 sensor senses the alcoholic content with breath.

In this paper, mainly two Arduino boards are used. One is placed on the helmet and the other on the bike. The FSR sensor force is applied on it. Once it gets pressed, it transmits the signal from transmitter to the receiver in bike. MQ3 sensor senses the alcohol content in breath simultaneously and if it exceeds the maximum tolerable range, it also transmits the signal to the receiver.

When any of these conditions is not satisfied, the bike would not start.

Figure 7 shows the image of a Helmet Unit. The Helmet is connected to the solar panel, where the power can be directly drawn from the sunlight when the process gets initiated.

Figure 7. Helmet Unit

Figure 8 shows the Bike unit when the process gets initiated from the Helmet unit. If the person is not wearing a helmet, the sensor detects and displays to wear helmet, where the bike would not start unless the person wears one (Figure 8(a)) and then alcohol detection sensor starts to work simultaneously and if the person has consumed alcohol then the sensor detects the presence of alcohol which is shown in Figure 8(b) and if the person wears helmet and have not consumed alcohol then the vehicle can be started which is shown in Figure 8( c).

Figure 8. (a) Wear Helmet, (b) Alcohol Detected, (c) Starting Vehicle

Severities of accidents have increased when a person either does not wear a helmet or drunken drive, which is problematic both to the rider and also the other drivers on the road. The authors have thus developed a smart helmet system that efficiently helps in preventing accidents due to the above reasons. Compared to the several existing systems, this system is very cost effective, making it affordable to every person. The system when implemented would be considered to be efficient and good.

The system shall be accepted globally by making it more compact with more advanced features. Government can enforce laws to install such systems in riding a two wheeler.