Find a project you can actually build
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.
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.
Object detection using AI
Object detection is a fundamental task in computer vision with wide-ranging applications in surveillance, automation, and smart environments. This project presents an AI-driven object detection system using an ESP32-CAM module integrated within an IoT and Flask-based architecture. The system captures live video feeds through the ESP32-CAM and transmits image frames to a Flask server for processing. Machine learning models, trained to recognize specific objects, are employed to perform real-time detection and classification. Upon detection, the results are displayed through a web dashboard, enabling remote monitoring and decision-making. The compact design of the ESP32-CAM makes the system highly portable and suitable for embedded smart surveillance, home automation, and access control applications. The use of lightweight ML models ensures efficient on-device inference or low-latency cloud processing, depending on the deployment configuration. This project demonstrates how combining AI, IoT, and Flask technologies can yield a low-cost, intelligent, and scalable solution for real-time object detection in dynamic environments.
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.
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.