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Working IoT, AI/ML and embedded builds with abstracts, component lists, documentation, and developer support — ready for your final-year submission.
Non invasive Measurement of cholesterol and haemoglobin using ppg
Traditional methods for measuring cholesterol and hemoglobin levels are invasive, requiring blood samples and laboratory processing, which may be inconvenient for continuous or remote health monitoring. This project proposes a non-invasive health monitoring system that estimates cholesterol and hemoglobin levels using Photoplethysmography (PPG) signals, combined with machine learning (ML) algorithms. The system is built using a NodeMCU microcontroller and integrates an SpO₂ sensor and heart rate sensor to capture real-time PPG waveforms. These signals are analyzed using a Flask-based server, where ML models trained on clinical datasets predict the user’s cholesterol and hemoglobin levels with reasonable accuracy. The results are displayed on a secure web dashboard for remote health monitoring. This contactless solution supports continuous assessment without needles or lab intervention, making it ideal for home use, remote clinics, and chronic care settings. It provides an accessible, low-cost alternative for routine health diagnostics while promoting user comfort and reducing healthcare burdens.
Smart Infant Incubator with Automated Oxygen and Temperature Control and Real-Time Alerts
Premature and critically ill infants require precise environmental conditions for survival and recovery. Traditional incubators often lack remote monitoring and intelligent control features, increasing the risk of manual error and delayed intervention. This project presents a Smart Infant Incubator System using IoT and Flask, designed for automated control of oxygen and temperature levels with real-time alerts to caregivers. The system is built around an ESP32 microcontroller, which continuously monitors vital parameters using a SpO₂ sensor to track blood oxygen saturation and a temperature sensor to ensure thermal stability inside the incubator. Based on real-time sensor data, a solenoid valve regulates oxygen flow, while a water pump is used for humidification or temperature adjustment mechanisms within the chamber. All data is transmitted to a Flask-based IoT dashboard, enabling remote visualization, threshold-based alerts, and historical trend analysis. This intelligent incubator improves neonatal care by reducing the need for constant manual supervision and enhancing response time during critical fluctuations in an infant’s condition.
IoT-Based Remote Patient Monitoring System using surrounding weather
The integration of IoT technologies into healthcare has enabled significant advancements in remote patient monitoring, especially for patients requiring continuous observation outside clinical settings. This project presents an IoT-based remote patient monitoring system that not only tracks vital health parameters but also correlates them with surrounding environmental conditions to provide more accurate and context-aware insights. The system is built around a NodeMCU microcontroller, which collects physiological data using temperature and SpO₂ sensors. These readings are wirelessly transmitted to a Flask-based cloud platform, where real-time monitoring and analysis are performed. The novelty of the system lies in its capability to incorporate ambient weather conditions into patient monitoring. Environmental factors such as temperature fluctuations can significantly affect patient health, particularly in individuals with respiratory or cardiovascular conditions. By integrating this additional layer of data, the system provides more informed alerts and health predictions. This approach enhances the reliability of remote health surveillance, making it suitable for deployment in home care, rural healthcare, and post-discharge patient management.
Waste to Energy: Arduino-Based Thermoelectric Energy Harvesting Using TEG Modules
With the growing global emphasis on sustainable energy solutions, the conversion of waste heat into usable electrical energy has emerged as a promising strategy. This project presents a waste-to-energy harvesting system utilizing Thermoelectric Generator (TEG) modules to convert heat energy into electricity. The system is built around an Arduino Uno microcontroller, which monitors and manages energy generation in real-time. Heat from waste sources is applied to the TEG modules, producing a DC voltage output, which is regulated using a buck-boost converter to maintain consistent voltage levels. An LCD (16x2) display provides live data on the voltage output, supported by a voltmeter for validation. LED indicators are used to demonstrate successful energy output and system status. The system illustrates a practical and scalable approach for micro-energy harvesting from ambient or industrial waste heat, aligning with green energy goals. Its simple design, low cost, and adaptability make it suitable for educational, industrial, and rural applications where waste heat is abundant and underutilized.
smart street Light
Conventional street lighting systems consume substantial energy and often remain powered regardless of environmental lighting conditions or pedestrian activity, leading to energy wastage. To address this issue, this project proposes an IoT-based Smart Street Light System designed to optimize energy usage by dynamically controlling street lights based on environmental and motion conditions. The system employs a NodeMCU microcontroller to collect data from an array of sensors, including an LDR (Light Dependent Resistor) sensor to detect ambient light levels and IR (Infrared) sensors to detect human or vehicular movement near the light pole. Based on sensor inputs, the system intelligently turns the LED light on or off via a relay module. A stick-mounted setup simulates real-world deployment of the street light pole. When ambient light is sufficient (e.g., during the daytime), the street light remains off, while in low-light conditions, it turns on only if movement is detected, thus conserving energy. This smart lighting solution represents a cost-effective and sustainable advancement in urban infrastructure, promoting intelligent energy management and public safety.
ambulance signal managing
Emergency vehicle delays due to traffic congestion remain a critical issue, often resulting in life-threatening situations. To address this challenge, this project presents an IoT and ML-based Ambulance Signal Management System that dynamically adjusts traffic signals to provide a clear path for ambulances. The system integrates NodeMCU and ESP32-CAM to detect approaching ambulances in real-time. The ESP32-CAM captures live video feeds at intersections, which are processed using machine learning algorithms deployed via a Flask-based backend to identify emergency vehicles with high accuracy. Upon detection, the system communicates with the traffic light control unit, mounted on a simulation stick setup, and automatically switches the signals to green in favor of the ambulance’s route. This not only reduces response time but also minimizes manual intervention and traffic confusion. The system logs event data and provides a real-time dashboard for remote monitoring and analytics. The solution is scalable, efficient, and ideal for integration in smart traffic infrastructure to enhance emergency response efficiency in urban areas.