Sensor-Based Safety Helmets: IoT Innovations for Crash Prevention

and Rider Protection

S. Karuppusamy, Sethupathi R, Sudharsan C. N and VimalSiva A

Department of Computer Science and Engineering, Nandha Engineering College, Erode, Tamil Nadu, India

Keywords: Smart Helmet, Alerting, Proactive Protection, Safety, Emergency Response.

Abstract: The purpose of this project is to improve motorcycle safety through state-of-the-art IoT integrated smart

helmets. Drivers must wear helmets to enable security features. As soon as an accident occurs, the system

immediately recognizes the effect and sends an emergency response. SOS services, nearby emergency

vehicles and up to 5 registered trustworthy people will be automatically alerted. The helmet provides

security before unauthorized use, as it is not permitted to start the engine when the vehicle carries it. The

application forces the helmet to counter the occurrence of a fatal injury. This security solution promotes

proactive protection and ensures a rapid rescue process. Individual drivers benefit a lot as they ensure that

someone is on the way to save themselves, even without a bystander to provide help.

1 INTRODUCTION

Motorcycle accidents, which account for a majority

of serious injuries and deaths worldwide, are

typically caused by slow emergency response and a

lack of protective gear. In view of all these issues,

this work will design smart helmets (Jesu doss A. et

al. 2019. integrated with IoT for driver’s safety. The

helmet has sensors that react instantaneously to

accidents and trigger automatic emergency

responses. Immediately send alerts to SOS services,

ambulances surrounding the setting of the collision,

and up to five registered contacts of the victim to

ensure instant access to medical assistance in an

event of a road traffic accident. Such automated

response systems can help mitigate injury and

enhance survival odds. Another intelligent helmet

capable of accident detection that does more than

accident detection it's also management in the parts

helmet and will not start the motorcycle machine.

Due to its simplicity and efficiency, Yolo model is

generally used for real-time object detection tasks.

Unlike other methods that use complex pipelines or

multiple machining stages, Yolo predicts class

probabilities and bounding boxes directly from the

entire image in a single forward pass through the

network. Besides reducing computing efforts, this

mode drives deeper real-time proficiencies. With

each new publication.

This helps maintain that security protocols are

provided 24/7 and reduces the risk of head injuries

that can prove fatal. The GPS prosecution also gives

responders the ability to pinpoint accurate scenes of

the accident, resulting in even more efficient

rescues. This system is particularly beneficial for

individual drivers as it guarantees instant assistance

even for the viewers. This smart helmet provides

predictive security solutions to make your

motorcycle trip safer by incorporating IoT

technology.

2 LITERATURE STUDY

In the last few years the generation of smart IoT-

based helmets has been an area of main attention in

research. Jesu Doss et al. Smart Helmet to Avoid

Accident (2019) that suggested using real-time

monitoring systems to protect riders. Likewise,

Mehata et al. developed an automated IoT-based

helmet for monitoring safety and health for workers

that included a data logging algorithm to monitor

safety parameters at the site. In the field of

transportation, Divyasudha et al. (IEEE) devised a

smart IoT-enabled low-cost helmet that prevents

accidents and increases emergency response. Due to

various other innovations in smart helmet

technology, Uniyal et al. researched and proposed an

Karuppusamy, S., R., S., N., S. C. and A., V. S.

Sensor-Based Safety Helmets: IoT Innovations for Crash Prevention and Rider Protection.

DOI: 10.5220/0013917000004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 4, pages

569-573

ISBN: 978-989-758-777-1

569

IoT-based system with a data logging mechanism

where it can log the activities of the rider and

environmental conditions.

Shabbeer and Meleet (2017) proposed smart

helmet based on which the system can detect the

accident and gives the alert message for providing

help in emergencies. Roja and Srihari (2018)

brought smart helmet applications to the mining

industry by developing a helmet that monitors air

quality, preventing exposure of workers to

hazardous conditions. Similarly, Behr et al. (2016),

focused on detecting hazardous events in mines to

provide real time alerts to improve the safety of a

worker. In addition to accident prevention and

environmental surveillance, Chandran et al. revolves

around Konnect, an IoT-enabled smart helmet

specifically designed to sense and inform accidents

so as to generate auto-alerts towards emergency

services. Aree buddin and Manoj (2017) have also

extended the smart helmets concept to beyond a

single sensor for the safety of riders (Divyasudha N

et al.) and real-time observation. Finally, Archana et

al. engineered an overall safety comfort system

integrating various cutting-edge elements to promote

riders’ full security. Collectively, these studies

highlight the role of IoT technology in smart helmet

design and provide solutions that enhance user

safety through immediate emergency response,

environmental awareness, and accident prevention.

3 METHODOLOGY

The methodology used to develop the IoT-integrated

smart helmet system involves a thorough

requirement analysis and planning stage that defines

all the functional and non-functional requirements of

the system from surveys, stakeholder consultations,

and existing technology analysis. This is followed

by the system design, which defines high-level

requirements for hardware and software

components. Hardware design focuses on the

selection of (Divyasudha N et al.) sensors, including

accelerometer, gyroscope and GPS, (Manish Uniyal

et al.) integration of a microcontroller for processing

data, (Shoeb Ahmed Shabbeer et al.) and

implementation of a communication module (GSM

or Bluetooth).

The software design includes algorithms for

accident detection, an interface (application in app,

web based) to interact with the users, and a cloud-

based backend for security. It is also integrated with

security features like RFID or biometric

authentication to prevent unauthorized use. Then,

after assembling the hardware components, writing

firmware for the microcontroller, and integrating

with the mobile application and cloud backend, a

functional prototype is developed. The prototype is

subjected to extensive testing and validation in

simulated accident scenarios under different

environmental conditions to verify performance,

accuracy and reliability. The system is deployed

when validated for real-world use, including

partnerships to integrate it into motorcycle ignition

systems and collaboration with emergency services

to expedite SOS responses. It also trains users on

how to use both the smart helmet and the

accompanying app. After the deployment, the

system enters an ongoing phase of monitoring and

maintenance to handle false alarms, optimization of

algorithms, and updates based on users’ feedback

and technology advancements. Finally, evaluating

the impact of the project through data collection on

accident detection rates, response times, and user

satisfaction, and sharing findings to encourage the

widespread adoption of the system (Shoeb Ahmed

Shabbeer et al.). This approach is designed to result

in a strong, dependable, and easy-to-use solution for

preventing motorcycle accidents through preemptive

protection and quick emergency response.

Figure1

shows Smart Helmet Accident Detection and Alert

System Architecture.

Figure 1: Smart Helmet Accident Detection and Alert

System Architecture.

4 SYSTEM IMPLEMENTATIONS

4.1 Hardware Requirements

4.1.1 Arduino Uno – Main controller

Arduino uno is a microcontroller board of

Electronics Project. It features 14 digital E/A pencils

(6 PWM), 6 analog inputs, and a 16-MHz-

ATMEGA328P chip It can use this to drive LEDs,

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

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read sensors, iterate electrical engines, and create

IoT devices. MikroC for PIC Microcontroller

Programming. (Example: On pin 13, the LED will

blink. Power supply through USB (5 V) or external

(7°12 V). AT I2C, SPI or daisy-chain sensor to

consult. Ideal for novices and experts alike. Figure 2

shows Arduino Uno. It is utilized for robotics,

automation, and prototyping. 5- More than 500 mA

shall not be drawn from the pin. Starting small

projects and moving to complex ones.

Figure 2: Arduino Uno.

4.1.2 FSR

The FSR (force sensing resistor) is used to sense the

physical pressure, squeezing. Their power

consumption is less. They are used for most touch

sensitive applications. They are low cost and less

weight. This sensor (figure 3) is configured with the

NodeMCU, then the data is being transmitted to the

cloud.

Figure 3: FSR sensor.

4.1.3 Vibrating Sensor

Vibrating sensor: The vibrating sensor (figure 4) is

also known as a piezoelectric sensor, when a band of

frequency created by the vibrating sensor based on

vibration, it measures pressure, temperature,

acceleration, force. Particularly it measures force

and damped vibration.

Figure 4: Vibrating Sensor.

4.2 Software Requirements

4.2.1 Arduino IDE

Arduino IDE is a free, open source software that is

used to write, compile and upload code to an

Arduino board. It is implemented by writing

program in C/C++ language. Now we collect the

errors and verify the code. Then upload the code to

your Arduino board. Or you can also use a serial

monitor to spy on the output and debug the program.

You will have the option to import libraries to give

your code even more functionality. Just select the

correct board type and the corresponding port and

you are good to go!

4.2.2 GSM Module

A GSM modem is a hardware device that enables

you to send and receive data, SMS, and voice calls

using a mobile phone connection. Connect GSM

modem to your computer/device using a USB cable

or serial connected. Install any required drivers and

software. Set modem on your mobile network like

APN, username, password, etc. Command to Send &

Receive SMS, Voice Calls, and Data It also enables

you to send and retrieve files, emails, and other data.

GSM modems are used in remote monitoring,

automation, and IoT projects. Ensure that your

modem has an active SIM card and mobile phone

signal

5 RESULT

IoT and intelligent helmets in the two-wheel safety

system increase the safety of the driver in the event

of an accident and avoid injury. Figure 5 shows End

Module. The accident is detected by checking the

comparison between the helmet binding and

Sensor-Based Safety Helmets: IoT Innovations for Crash Prevention and Rider Protection

571

predefined limits. The system sounds an alert or

takes a security measure if the tilt corresponds to a

fall in the helmet. Its innovative solution is based

only on the driving's behavior and becomes a trusted

and trusted security mechanism. Figure 6 and figure

7 shows Output interface.

Figure 5: End Module.

Figure 6: Output interface.

Figure 7: Output Interface.

6 CONCLUSION & FUTURE

ENHANCEMENT

To sum up, Two Wheel safety system is a

remarkable innovation in driver safety project

integrated into a smart helmet. The actual time data

as well as the tilt value is used to determine if an

accident has occurred, based on whether or not the

helmet button passes the predefined threshold. You

will write this to trigger immediate alerts or actions,

so that you are notified quickly. You don't have to

worry about carrying it if you are not driving or

without using the service. The latest design also

aims to minimize bike and motorcycle injuries and

overall road safety. With advanced IoT technology

along with some functional security features, this

intelligent helmet system solution is not only an

efficient way to avoid accidents but also acts as a

safety net for the riders. This is a hopeful move

towards making the streets safer and the risk of

travelling on two wheels lower.

Future updates for the smart helmet system

include AI analytics either to predict accidents

through riders' behavior and which can be read by

the helmet, avoiding potential accidents. Low-power

sensors providing constant functioning can be

motivated by solar charging or enhanced battery

efficiency. Voice and gesture commands integration

will allow hands-free operation of safety features for

improved convenience. A self-locking safety helmet

is another solution that can be integrated. When the

rider wants to start the bike, this mechanism would

be locked with the ignition system of the bike, and to

start it, the rider must put on the helmet, enhancing

safety and ensuring compliance. These capabilities

will make the system more intelligent, more efficient

and more user friendly.

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