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IoT Capstone Project – Sensor Integration and Real-Time Data Visualization

Overview

This repository documents my contributions as an IoT Developer for a capstone project focused on real-time environmental and physiological monitoring. The goal was to integrate multiple sensors into a unified system that collected, processed, and visualized data in real time to support intelligent decision-making and research applications.

As a junior member of the team, I was responsible for configuring, testing, and integrating key sensors and ensuring reliable communication between hardware and software layers.


Key Contributions

1. DHT Temperature & Humidity Sensor

  • Configured and calibrated the DHT11/DHT22 sensor for accurate temperature and humidity readings.
  • Ensured stable data transmission between the sensor and the Arduino microcontroller.
  • Verified sensor precision through multiple test runs and environmental simulations.

2. MAX30102 & MAX30205 Sensor Modules

  • Successfully implemented MAX30102 (heart rate & SpO₂ sensor) and MAX30205 (body temperature sensor).
  • Resolved initialization and signal acquisition issues, ensuring reliable sensor performance.
  • Provided technical assistance to teammates for integration into the combined IoT pipeline.

3. Serial Communication & Real-Time Data Logging

  • Used Arduino IDE to open and monitor the COM port for continuous sensor data streaming.
  • Wrote a serial listener program to read live sensor values from the COM port and store them for analysis.
  • Implemented Python scripts for real-time plotting of temperature, humidity, and heart rate data, visualizing live trends and anomalies.

Tools & Technologies

Hardware: Arduino Uno, DHT11/DHT22, MAX30102, MAX30205
Software: Arduino IDE, Python, Matplotlib, Serial Library, Power BI (for visualization)
Protocols: UART Serial Communication
Version Control: Git, GitHub


Outcome

This project achieved a real-time IoT monitoring system that could track environmental and human physiological parameters simultaneously. The reliable data transmission and live plotting interface provided a foundation for future predictive analysis and intelligent anomaly detection systems.


Acknowledgement

Worked collaboratively under the supervision of the project leads and IoT team members, contributing to the successful completion and testing of all sensor modules within the capstone prototype.

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