The Wearable UV Exposure Monitor is an AI + IoT project that combines Motion sensing and AI activity recognition, built with ESP32. The project follows a clean, modular pipeline where sensor data acquisition, AI processing, and presentation stay separated, making it easy to test, extend, and present. It showcases practical AI and IoT techniques while producing a working, demo-ready system.
Manual monitoring and decision making for this task is slow, inefficient, and cannot scale across many devices. Without an AI + IoT approach built on AI activity recognition and ESP32, users cannot collect data from the physical world and act on it intelligently in real time.
This project applies AI + IoT techniques through Motion sensing, orchestrated with ESP32 and AI activity recognition. The system is designed for reliability and intelligence, with sensing, connectivity, AI analysis, and clear evaluation. It produces consistent, reusable results and can be adapted to related smart applications with minimal changes.
ESP32
Heart rate sensor
ESP32 / Arduino / Raspberry Pi
MQTT / HTTP
Cloud dashboard
Machine learning models
MPU6050
Modular IoT pipeline around Motion sensing and AI activity recognition
Configurable sensor and AI settings
Real-time data collection and monitoring
Clear alerts, logging, and error handling
Reusable components for related smart systems
AI based decision and prediction
The project is layered: the sensing layer acquires data through Motion sensing; the connectivity and AI layer processes it with ESP32 and AI activity recognition; and the output layer monitors and presents results via Bluetooth sync. Shared connectivity, alerting, and dashboard modules support all layers, keeping the system robust and easy to extend.
Set up the hardware, sensors, and development environment.
Build the sensor data acquisition layer with Motion sensing.
Implement connectivity and AI processing using ESP32 and AI activity recognition.
Add the dashboard and alert layer via Bluetooth sync.
Wire up end-to-end flows and add error handling and retries.
Test with real data, tune AI models, and refine the system.
Package the project, document it, and prepare the demo and viva report.
Build production-style AI + IoT applications
Apply Fitness dashboards and Reading motion sensors
Connect and control IoT hardware
Analyze sensor data with machine learning
Present and defend a complete AI + IoT project in viva
Add edge AI for on-device decisions
Add more sensors and cloud services
Add mobile app control
Deploy on scalable cloud infrastructure
The Wearable UV Exposure Monitor delivers a complete AI + IoT workflow — from sensor data acquisition and connectivity to AI analysis and presentation. It is practical, modern, and easy to explain, making it an excellent final year project that demonstrates in-demand AI and IoT skills.
