🩺 Smart Health Monitoring System with ESP32, AI Analytics & Real-time Dashboard Real-time heart rate and SpO2 monitoring system using ESP32 sensors, AI-powered health analysis, and modern web technologies. Features secure device authentication, personalized health recommendations, and comprehensive data visualization.

This project focuses on developing a health monitoring system integrated with an AI powered chatbot to predict human diseases. The system continuously gathers vital signs and health-related data from users through wearable sensors or manual inputs. It analyzes symptoms and patterns using machine learning algorithms trained on medical datasets.

AI-powered system for real-time monitoring of crop health, soil conditions, and pest risks using multispectral/hyperspectral imaging and sensor data. The project analyzes vegetation patterns, soil parameters, and anomaly signals to detect diseases early, optimize irrigation, and improve overall farm productivity with data-driven insights.

An AI-powered wearable health monitoring system that collects real-time health metrics (heart rate, steps, calorie expenditure) using wearable sensors, processes data with a Random Forest Regressor, and provides personalized health recommendations and predictive insights via an interactive user interface.

AI-powered real-time health monitoring system that analyzes wearable sensor data at the edge for intelligent vital insights.

An AI-powered system for real-time health monitoring and geo-tracking, designed for military personnel and travelers. Features include sensor data collection, GPS tracking, AI-based health risk prediction, and multi-mode communication.

An advanced, real-time air quality monitoring and prediction system powered by AI and modern web technologies. Features live data updates, interactive maps, historical trends, and comprehensive health recommendations.

This AI-Powered Health Monitoring System is our PLP Final AI Project, designed to track real-time health metrics using wearable data. It detects anomalies like irregular heart rate or low oxygen and offers personalized recommendations, promoting preventive care and SDG 3 – Good Health and Well-being.

Indoor air quality impacts health and productivity, yet most existing monitoring systems provide raw data (e.g., CO2 or PM2.5 levels) without actionable insights. This project aims to build an AI-powered system that interprets air quality data and offers recommendations for improvement.

This app is designed to provide real-time monitoring and diagnostics for electric vehicle systems by wirelessly collecting sensor data—such as battery, motor, and CAN bus parameters—from an ESP32 device. The main goal is to help users easily track their vehicle’s health, visualize live data and trends, receive AI-powered fault predictions and expla

Developed an AI-powered IoT health monitoring system using STM32, MAX30102, and JFC103 sensors to measure heart rate, SpO₂, and blood pressure in real time, integrating MySQL and ERNIE LLM for data analysis and personalized health recommendations.

AirSafeNet - AI powered air quality monitoring and early warning system using Big Data to predict pollution and protect public health in smart cities. 🌍

SysHealth+ is an AI-powered predictive system health monitoring software that analyzes live system data to predict failures before they occur. It identifies failing components, detects anomalies, provides repair recommendations, and explains insights using an AI assistant—going beyond traditional system monitoring tools.

AICTE Internship Edunet : AI-powered soil health monitoring and management system using ML models, sensor + satellite data, and a web dashboard for real-time predictions and fertilizer recommendations.

HeartSafeAI — AI-powered real-time heart health monitoring and cardiac risk detection system. Preprocess sensor data, detect anomalies with deep learning, and provide live alerts to prevent heart attacks.

AI-powered health monitoring and early warning system for predicting waterborne disease outbreaks in rural Northeast India. Uses XGBoost ML (88.8% accuracy) to analyze environmental data and sends automatic email alerts to health workers and citizens.

A real-time AI Health Monitoring system that integrates ESP32-based physiological sensors (Heart Rate, SpO₂, and GSR) with a web dashboard to predict blood glucose levels using machine learning models. The system features Bluetooth-based data acquisition, live visualization, and a Flask-powered AI inference backend.

It is a smart healthcare monitoring system that combines modern technology with traditional Ayurvedic wisdom. It features IoT integration, real-time health alerts, AI-powered analytics, and secure data encryption to offer a holistic and secure healthcare experience

An AI-powered web system that monitors and analyzes microbial soil health using sensor data and machine learning. It provides real-time insights and smart recommendations to enhance soil quality and agricultural productivity.

An intelligent financial planning and strategic simulation platform for small business cash flow management, risk identification, and data-driven decision making. Features QuickBooks integration, AI-powered scenario planning, bank statement verification, and predictive analytics with a traffic light system for financial health monitoring.

Aegis is an AI-powered distributed healthcare monitoring system that processes real-time patient data to predict health risks. Built with cloud-native microservices, it ensures scalability, fault tolerance, and secure data flow while providing a smart dashboard for real-time insights and early medical alerts.

This project is an AI-powered system designed to monitor the health of satellites and protect them from potential threats like asteroids. It continuously analyzes satellite data and predicts risks to ensure safe and efficient space operations.

CrackX is an AI-powered road damage analysis system using YOLOv8 to detect cracks, potholes, and surface defects from images. It computes severity scores, repair priorities, and a Road Health Index to support smart infrastructure monitoring, maintenance planning, and data-driven urban decision making.

This project is an AI-powered predictive maintenance system that monitors the health of industrial machines using real-time sensor data. It detects anomalies and predicts potential failures to reduce downtime ,maintenance costs and increases safety of workers.

CropSense: An AI-Powered Crop Recommendation System An innovative project that integrates Arduino-based NPK sensors with machine learning models to analyze soil nutrient levels (Nitrogen, Phosphorus, Potassium) and provide optimal crop recommendations. Designed to enhance sustainable farming and soil health monitoring through data-driven insights.

The Explainable Medical Cyber-Physical System (MCPS) is an AI-powered(Deep Learning, ML , XAI) tool for analyzing ECG data through multiple modalities: real-time signal monitoring, file-based analysis, and paper ECG image analysis. Built for global health equity, it provides interpretable, lead-specific ECG Analysis.

Arrythmia AI is an AI-powered ECG disease detection system that uses convolutional neural networks (CNN) to analyze electrocardiogram data and identify potential heart conditions. Designed for quick and accurate diagnosis, helps healthcare providers and patients monitor heart health efficiently through advanced machine learning models.

AI-enabled Battery Management System monitors battery health, alerts users of hazards, and minimizes power loss. Partnerships with ThingSpeak and Pushbullet ensure real-time data and notifications. Aim: revolutionize battery management for diverse applications.

The AI-Powered Gardening System uses AI and IoT to monitor plant conditions and automate watering. It collects real-time data like soil moisture, temperature, humidity, and air quality, then uses machine learning to decide watering needs, reducing water waste, improving plant health, and minimizing manual effort.

HealS is an AI-enabled wellness system consolidating mental health, BMI tracking, and academic schedules. It uses an OpenAI-powered module to analyze student data and generate personalized wellness tips. Counselors monitor mood trends for intervention, while Admins manage the secure, integrated digital platform.