The Remote Patient Health Dashboard is an AI + IoT project that combines AI anomaly detection and Data logging, built with Temperature sensor. 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 Data logging and Temperature sensor, users cannot collect data from the physical world and act on it intelligently in real time.
This project applies AI + IoT techniques through AI anomaly detection, orchestrated with Temperature sensor and Data logging. 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.
Temperature sensor
Blynk / MQTT
ESP32 / Arduino / Raspberry Pi
MQTT / HTTP
Cloud dashboard
Machine learning models
ESP32 / Raspberry Pi
Heart rate sensor
Modular IoT pipeline around AI anomaly detection and Data logging
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 AI anomaly detection; the connectivity and AI layer processes it with Temperature sensor and Data logging; and the output layer monitors and presents results via Vital sensor node. 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 AI anomaly detection.
Implement connectivity and AI processing using Temperature sensor and Data logging.
Add the dashboard and alert layer via Vital sensor node.
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 Analyzing vitals with AI and Detecting anomalies
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 Remote Patient Health Dashboard 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.
