Smart Remainder (IoT + ML)
Smart Remainder is a personalized reminder system that combines the power of the Internet of Things (IoT) and Machine Learning to help users avoid forgetting their personal belongings in everyday environments. It was designed to be a simple, low-cost, and efficient solution that fits naturally into daily routines. The system detects when a user leaves a space—like a seat or desk—and checks if any important items such as bags, books, or laptops have been left behind. If it detects something, it immediately alerts the user through a buzzer sound and a mobile notification.
At its core, Smart Remainder uses an IoT-enabled microcontroller such as an ESP32, Raspberry Pi, or Arduino UNO to process real-time sensor data. A pressure sensor is used to monitor the user’s presence, while a camera with object detection capabilities (using the YOLO algorithm) identifies whether any items remain on the desk once the user has left. The system also integrates motion and thermal sensors to improve detection accuracy and minimize false alarms. In addition, environmental sensors like CO₂ and temperature modules help monitor the surrounding conditions, making the system versatile and adaptable for both home and office environments.
The software side of the project is developed using Arduino IDE for hardware programming and Python for data processing and image recognition. Libraries such as OpenCV are used to perform object detection, and the Blynk IoT mobile application is integrated to provide instant notifications and remote monitoring. When a user leaves their seat, the system automatically activates the camera, analyzes the visual input, and sends a real-time alert if it finds that an object has been forgotten. This dual alert mechanism—combining local buzzer feedback with remote mobile notifications—ensures that the user never misses an alert, even if they are away from their workspace.
Testing showed that Smart Remainder achieved a detection accuracy of around 92%, with an average system response time of about 1.2 seconds. The false alarm rate was limited to roughly 8%, which can be further reduced through machine learning optimization. Users appreciated the quick and reliable response of the system, especially in real-world conditions with varying light levels. The system’s modular design also allows for additional sensors or functionalities to be added easily, enabling future expansion for larger setups such as smart classrooms or office environments.
In the future, Smart Remainder can be enhanced with voice-based reminders, predictive analytics that anticipate commonly forgotten items, and integration with cloud-based systems or smart home assistants. These improvements would make the system more intelligent and personalized over time, helping it adapt to individual user behavior.
The mobile integration through the Blynk IoT platform allows users to receive notifications on their phones and customize alert preferences according to their needs. This makes the system not just reactive but also user-friendly and convenient. Installation is straightforward: the hardware components are connected to the microcontroller, and the software is set up using Arduino IDE and Python. The system requires only basic libraries such as pyserial, requests, and opencv-python, and it can be linked to the Blynk app through a unique authentication token.
Smart Remainder was developed by Ajith T, Abishek G, and Aaron Winston A, from the Department of Computer Science and Engineering at Panimalar Engineering College. Ajith focused on system design and hardware integration, Abishek worked on software development and algorithms, and Aaron contributed to testing and analysis. The project is based on their IEEE paper, “Design of a Personalized Smart Reminder System Utilizing IoT-Enabled Devices and Machine Learning for Efficient Routine Management” (2024), which details the system’s design, implementation, and performance analysis.
In conclusion, Smart Remainder is more than just a reminder system. It represents a step toward smarter living environments that can adapt to human behavior and reduce everyday mistakes. By combining IoT hardware, computer vision, and real-time alerts, the system helps users stay organized, avoid losses, and build more mindful routines. It’s an accessible, scalable, and intelligent approach to bringing everyday convenience through technology.