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Comprehensive Biomedical Monitoring System with Triple Parameter Surveillance
The advancement of IoT-based biomedical systems has paved the way for real-time remote patient monitoring, offering crucial support in medical emergencies and rural healthcare. This project proposes a Comprehensive Biomedical Monitoring System with Triple Parameter Surveillance, which continuously monitors the patient's blood oxygen level (SpO₂), body temperature, and real-time location, and integrates an automated alert mechanism through GSM communication. The system is built using an ESP32 microcontroller, which interfaces with a MAX30102 SpO₂ sensor, a temperature sensor, and a GPS module to collect vital physiological and location data. These parameters are wirelessly transmitted to a Flask-based web server, where the data is displayed on a live dashboard and analyzed using a machine learning model to detect abnormal health conditions such as hypoxia, fever, or sudden changes in location. If any health parameter crosses a predefined threshold, the system immediately activates the GSM module (SIM800L/SIM900) to send an emergency SMS alert containing the patient’s vital data and GPS location to medical responders or family members. This ensures quick response even in areas with poor internet connectivity. By integrating biomedical sensors, intelligent decision-making via machine learning, and dual communication through Wi-Fi and GSM, the system offers a reliable, portable, and scalable healthcare monitoring solution. It is particularly suited for remote patient care, mobile health units, elderly monitoring, and post-operative recovery tracking, where timely alerts and accurate health data are essential for saving lives.
A Navigation and Reservation Based Smart Parking Platform Using Genetic Optimization for Smart Cities
Urbanization has led to a surge in the number of vehicles, resulting in significant challenges in parking management, especially in smart cities. Conventional parking systems often suffer from congestion, time delays, and inefficient space utilization. To address these issues, this project proposes a smart parking platform that integrates real-time navigation and reservation with Genetic Optimization algorithms for space allocation. The system is built using an Arduino Uno microcontroller interfaced with IR sensors to detect the availability of parking slots, a 20x4 LCD display to provide user instructions and slot availability, and an RFID card reader to authenticate and identify users. When a vehicle approaches, the user is authenticated via the RFID card, and available slots are checked using IR sensors. The system reserves the most optimal parking slot based on predefined conditions such as proximity to entry, user preferences, or slot rotation logic. To enhance the allocation process, a Genetic Optimization Algorithm is used at the backend (on a connected server or simulated in software) to determine the most efficient slot assignment, minimizing time and space conflict while maximizing user convenience and parking space usage. This integration of embedded hardware and intelligent algorithmic decision-making paves the way for a highly scalable, automated, and user-friendly smart parking solution. The system contributes to reduced traffic congestion, efficient urban space management, and enhanced user experience, making it ideal for deployment in smart cities.
Alcohol Detection-based Vehicle Control & Automatic Driver Drowsiness Alert System using Python Flask with GPS (Helmet-based)
Road safety has become a critical issue due to the increasing number of accidents caused by drunk driving, driver fatigue, and lack of protective gear. To address these challenges, this project presents an integrated system titled “Alcohol Detection-Based Vehicle Control and Automatic Driver Drowsiness Alert System” utilizing the capabilities of IoT, embedded systems, and sensor fusion. The proposed system employs an ESP32 microcontroller as the core unit, interfaced with an alcohol sensor (MQ-3/MQ-7) to detect intoxication, a SpO₂ sensor (MAX30102) to monitor blood oxygen and heart rate, and an eyeblink sensor to determine signs of driver fatigue. Additionally, a helmet detection module ensures the rider's compliance with safety regulations by preventing ignition in the absence of a helmet. For location tracking and emergency alerts, a GPS module is integrated, enabling real-time positioning. The system communicates with a Python Flask-based web server that logs sensor data, displays driver vitals, and generates alerts when thresholds are breached. All hardware components are mounted on a custom-designed PCB board, ensuring compactness and durability. In operation, if alcohol is detected, the ignition is disabled, preventing the vehicle from starting. Similarly, in cases of drowsiness or abnormal SpO₂ readings, the system issues audio-visual alerts and notifies connected monitoring systems via the Flask server. This multi-sensor, real-time safety system demonstrates an effective approach toward reducing road accidents by enforcing driver fitness checks, promoting helmet usage, and enabling live health and location tracking.