Materials
ESP8266 MODULE:
The ESP8266 shown is a low-cost, Wi-Fi-enabled microcontroller widely used in the IoT industry. Its compact size, low energy consumption and ease of use have made it popular. ESP8266 is connected to our sensors online and transfers data to our cloud database via Wi-Fi. The ESP8266 acted as a gateway between our sensors and the cloud, making it possible to view and analyze data in real time. Its small form factor and low energy consumption made it ideal for use in our project, where space and energy efficiency were important factors. Overall, the ESP8266 was key to the success of our project, enabling us to develop a robust and effective driver safety monitoring system.
Max30102 (Pulse oximeter sensor):
The MAX30102 is an optical sensor which reads the heart and blood oxygen level within the blood. It consists of an LED, photodetector and signal processing module. The sensor is capable of working in low energy mode permitting it to be best for battery-powered and portable applications. In our project, the MAX30102 sensor was used to display the driver's heart rate and oxygen saturation level.
MLX90614 (Temperature sensor):
The MLX90614 is a non-contact infrared temperature sensor which can measure the temperature without contact. In our project MLX90614 to monitor the driver’s temperature, which could be an indicator of health conditions or fatigue. The sensor is connected to an ESP8266 microcontroller, which transmits the temperature readings to the cloud database for further evaluation. By tracking the driver’s temperature, results could spot any unusual variations and take action to prevent accidents.
​MQ3 (Breathalyser):
MQ-3 is a gas sensor that detects the presence of alcohol vapors in the air. It was used in the task to determine the consumption of alcohol by the driver. When alcohol vapors are detected, the sensor sends data to the ESP8266 module, which then alerts the driver through an app. The MQ-3 sensor works by measuring the electrical conductivity of the air, which is modified when it comes into contact with alcohol. This sensor is a critical component of the driver safety system, it can help save from accidents resulting from use of alcohol.
Wemos d1 Mini:
The Wemos D1 Mini is a compact and versatile WiFi-enabled microcontroller that became used in our project to manipulate and study statistics from the MLX90614 temperature sensor. It is programmed to collect accelerometer readings and send them to the cloud for analysis. The Wemos D1 Mini is a popular choice for IoT applications due to its small length, low power consumption, and compatibility with the Arduino programming surroundings. Its WiFi competencies allow for smooth connectivity to the internet and cloud services, making it an ideal preference in your challenge.
ADXL345 Accelerometer:
The accelerometer sensor ADXL345 tracks acceleration forces in all three axes. It is useful for motion detection applications since it can recognise both static and dynamic acceleration. In our study, the ADXL345 was used to measure the acceleration forces of a vehicle during a collision in order to detect crashes. For additional analysis and crash evaluation, the ADXL345 data was transferred to the cloud.
Web-Camera:
A web camera is a type of video camera that takes pictures and videos and transfers them to a computer system. In our project, we captured the driver's image with a web camera, which was subsequently analyzed by a computer vision algorithm. The computer vision program recognizes the driver's face and monitored their eye movements, awareness, and tiredness. Based on this data, the system provided notifications to the driver to help him stay attentive and avoid accidents.
Software Used
​Firebase:
​Firebase is a cloud-based platform offered by Google that provides developers with various tools and services for developing web and mobile applications. One of its key features is a real-time database that enables the synchronization of data between clients and servers in real-time. Additionally, Firebase provides authentication services that simplify the process of adding user authentication to applications. Our project utilized Firebase to monitor driver and vehicle safety by connecting the system to the Firebase database. This allowed authorized observers to remotely monitor the driver's state and receive real-time alerts if any safety issues arose.
​Computer vision:
Computer vision is a subfield of artificial intelligence and computer science that deals with enabling computers to interpret and analyze images and videos from the real world. It is used to extract information from images and videos, such as object detection, face recognition, and motion analysis. In our project, we used computer vision techniques to analyze the driver's image captured by the webcam and determine whether they were alert or drowsy. The images were processed using OpenCV, a popular computer vision library, to detect and track facial landmarks such as the eyes and mouth, and determine the driver's level of drowsiness using a custom algorithm.
OpenCV: OpenCV (Open Source Computer Vision) is a free and open-source library of programming functions mainly aimed at real-time computer vision. It has a vast collection of pre-built algorithms for image processing, object detection, and tracking. In our project, utilization of OpenCV to process the video input received from the webcam. Specifically, use of OpenCV to encode and decode the images, detect the driver's face and eyes, and track facial landmarks for monitoring the driver's alertness. The integration of OpenCV with our system has enabled us to develop a robust and reliable driver monitoring system.
​Arduino IDE:
The Arduino Integrated Development Environment (IDE) is an open-supply software program used for programming Arduino forums. It provides a user-friendly interface to upload code to the forums, and consists of a number of libraries for distinct sensors and modules. In our undertaking, we used Arduino IDE to software the ESP8266 module to collect facts from the sensors.