An Arduino Nano controls the LED sequencing and acquires optical signals, which are transmitted to a Raspberry Pi 3 A+ for processing. The system uses the Non-Negative Least Squares (NNLS) algorithm and machine learning techniques to estimate Haemoglobin levels accurately without gender-based calibration.

To enhance reliability, an accelerometer detects motion artifacts, while a DS18B20 temperature sensor monitors temperature variations. The system continuously evaluates signal quality using the Signal-to-Noise Ratio (SNR) and displays Haemoglobin concentration, SNR, temperature, and motion status on a 16×2 LCD. The proposed system provides a safe, reusable, and real-time solution for non-invasive Haemoglobin monitoring and health assessment.

Objectives:

1. To develop a non-invasive system for measuring Haemoglobin concentration.
2. To use seven optical wavelengths for accurate Haemoglobin estimation.
3. To acquire and process optical signals using Arduino Nano and Raspberry Pi.
4. To implement the NNLS algorithm for Haemoglobin calculation.
5. To improve measurement accuracy using machine learning techniques.
6. To monitor signal quality using Signal-to-Noise Ratio (SNR) analysis.
7. To detect motion artifacts using an accelerometer sensor.
8. To measure temperature variations using a DS18B20 temperature sensor.
9. To display Haemoglobin level, SNR, temperature, and motion status on an LCD.
10. To provide a safe, reusable, and real-time Haemoglobin monitoring solution.

The major building blocks of this project are:

• Power supply.
• RASPBERRY pi3 A+.
• Arduino NANO.
• Noninvasive Haemoglobin Sensor.
• LCD display.
• Gyroscope.
• Temperature sensor.
• SD card.

Software’s used:

• Arduino IDE.
• Embedded C language.
• Raspbian OS.
• Python Language.
• Machine Learning.

video:

The CanSat integrates multiple sensors, including an air quality sensor, BMP180 barometric pressure sensor for temperature and pressure measurement, a gyroscope for orientation detection, and the INA219 current sensor for power monitoring. A GPS module provides real-time location tracking, while an MMC card is used for onboard data logging. Wireless communication between the CanSat and the ground station is achieved using XBee modules, enabling reliable telemetry transmission.

Power is supplied by a battery system regulated through the LM2596 voltage regulator to ensure stable operation. The system also includes actuators such as a servo and DC motors controlled via a driver circuit, along with a buzzer for status indication.

During operation, the CanSat collects atmospheric parameters such as temperature, pressure, altitude, and air quality, processes the data onboard, and transmits it to a PC-based ground station for monitoring and analysis. The proposed system demonstrates an efficient, scalable, and cost-effective approach to environmental data acquisition, making it suitable for educational purposes and preliminary aerospace research.

Objectives:

• To design and build a compact CanSat system using a Teensy 4.0 microcontroller.
• To measure environmental parameters such as temperature, pressure, altitude, and air quality using sensors like BMP180 barometric pressure sensor.
• To monitor system power using the INA219 current sensor.
• To obtain real-time location data using a GPS module.
• To transmit collected data wirelessly to a ground station using XBee modules.
• To store sensor data locally on an MMC/SD card for backup.
• To provide alerts using a buzzer and control mechanisms like servo and DC motors.
• To develop a low-cost and efficient weather monitoring system for educational and research purposes.

Components:

• Battery power.
• LM2596.
• Teensy 4.0 microcontroller.
• Air quality sensor.
• BMP180.
• GYROSCOPE
• IA219 module.
• GPS
• DC Motor.
• Buzzer.
• Servo motor.
• XBEE Transmitter and Receiver.

Software’s used:

• Arduino IDE for compiling and dumping code into Microcontroller
• Express SCH for Circuit design.

video: