SMART HELMET
Senior Project 2025–2026
Senior Project 2025–2026
Protect workers with real-time gas detection, smart alerts, and automatic emergency response — all inside one intelligent wearable device.
Real-time demonstration of the Smart Helmet prototype, gas detection workflow, OLED display, and automatic fan response.
The Smart Helmet is a wearable IoT safety system that continuously monitors air quality around workers. It detects harmful gases, displays live readings, alerts the worker, and sends real-time data to a monitoring dashboard.
The system is built into a helmet so workers can carry protection with them while moving inside industrial areas such as factories, gas stations, and oil environments.
Sensor readings are processed by the ESP32 and sent through Wi-Fi using MQTT, allowing supervisors to monitor safety conditions remotely.
When dangerous gas levels are detected, the system activates a stronger alarm and automatically turns on a fan through a relay-based motor system.
Gas leaks in industrial environments are dangerous because harmful gases are not always monitored continuously. This can cause health risks, fire hazards, equipment damage, and financial losses.
Workers may be exposed to gases that are colorless, odorless, or difficult to detect manually. Without early warning, a small leak can become a serious emergency.
The proposed solution is a smart helmet that continuously checks air quality, classifies the environment into SAFE, WARNING, and DANGER states, and provides immediate alerts.
The project combines hardware, software, and IoT communication to create a practical safety system for real-time gas monitoring.
Provide early warnings when gas levels rise and help workers react before the environment becomes highly dangerous.
Display sensor values on both the OLED screen and dashboard for continuous monitoring without manual checking.
Send gas readings through Wi-Fi and MQTT so supervisors can monitor the helmet from a dashboard interface.
Use a buzzer and warning states to alert the worker when gas levels exceed predefined thresholds.
Automatically activate a motor fan in DANGER mode to help reduce gas concentration around the worker.
Test the helmet, motor, server, and dashboard under different scenarios to verify system performance.
The system contains two main circuits: the helmet circuit for sensing and alerts, and the motor-fan circuit for emergency ventilation. The server connects the devices to the dashboard through MQTT and WebSocket communication.
MQ2, MQ9, MQ136, MQ137, CO₂, and O₂ sensors collect air-quality readings.
Processes readings, checks thresholds, controls OLED and buzzer alerts.
Mosquitto receives sensor data and sends fan control commands.
Displays real-time sensor readings, system status, and multiple helmets.
ESP32 and relay module activate the fan automatically in DANGER mode.
The project was implemented using hardware circuits, embedded software, server configuration, and a web dashboard for live monitoring.
The helmet includes gas sensors, an ESP32, OLED display, buzzer, USB-C PD power module, voltage dividers, capacitor, and switch. It reads air-quality data and reacts based on the safety state.
The fan system uses a second ESP32, relay module, DC motor, battery pack, and diode. It receives MQTT commands and turns on when a DANGER condition is detected.
An Ubuntu server VM was configured with Mosquitto MQTT broker, WebSocket support, user authentication, ACL permissions, and a Python HTTP service to host the dashboard.
The embedded code was written in Arduino IDE using C++. The dashboard was created using HTML, CSS, and JavaScript with real-time MQTT/WebSocket updates.
A real photo of the prototype during presentation, showing the Smart Helmet circuit mounted on the helmet as a wearable safety system.
This section shows the final prototype after mounting the controller and sensors on the helmet to demonstrate the system practically.
This dashboard simulation shows how sensor values are displayed in real time. In the actual system, readings are received from the ESP32 through MQTT and WebSocket communication.
The helmet automatically classifies the environment into three safety levels based on sensor readings and predefined thresholds.
Gas readings are within normal limits. Sensor values are shown on the OLED screen and dashboard while the system continues monitoring.
Gas levels start increasing. The buzzer turns on to alert the worker that the environment may become unsafe.
Gas levels exceed danger thresholds. The system activates a stronger alarm and automatically turns on the ventilation fan.
A clean visual representation of the Smart Helmet logic and the automatic fan control workflow.
ESP32 sensing, alerts, OLED display, and MQTT data flow
Wi‑Fi, MQTT subscription, fan command, and system loop
Real components used in the Smart Helmet system and the fan-control circuit.
Full hardware setup showing the sensing unit, ESP32 controller, power system, relay module, and the active fan response used during system testing.
Close-up view of the gas-sensor circuit, ESP32 processing unit, buzzer, battery power, and fan-control electronics integrated into the prototype.
Continuous environmental monitoring.
Readings sent for live monitoring.
Buzzer response during unsafe levels.
Automatic ventilation in danger mode.
Designed to protect workers.
The system was tested through hardware, software, dashboard, and full-system scenarios to verify that all components work together correctly.
The helmet circuit was tested through power-on, OLED loading, sensor warm-up, server communication, and gas detection scenarios.
The motor circuit was tested with power supply, relay control, Wi-Fi connection, and remote fan activation.
The dashboard was tested in SAFE, WARNING, and DANGER modes with real-time visualization and fan control.
The server was tested through WebSocket connection states, MQTT broker operation, and Ubuntu VM access.
The complete system successfully detected gas, updated the dashboard, triggered alerts, and activated the fan during dangerous conditions.
Although the system achieved its main goal of gas monitoring, alerting, and automatic response, the Smart Helmet can be improved in the future with more advanced features to increase safety and reliability in industrial environments.
Add GPS to identify the worker’s location during dangerous situations, helping supervisors respond faster and locate the affected worker inside the industrial site.
Develop a mobile application that displays sensor readings, alerts, and helmet status in real time, allowing workers and supervisors to monitor the system easily.
Use artificial intelligence to analyze sensor readings and predict dangerous conditions before gas levels reach a critical stage.
Improve power consumption and add a more efficient battery so the helmet can operate for longer periods during continuous use in work environments.
University of Bahrain — College of Information Technology — Department of Computer Engineering
Senior Project Student
Senior Project Student
Senior Project Student
Dr. Alaudeen Yousif Alomari