Smart Classroom Monitoring System for Faculty Presence Detection Using Multi-Modal IOT and AI Techniques

Abstract

In many educational institutions, ensuring that faculty members are consistently present in classrooms remains a challenge. Traditional attendance systems are often limited to entry-level verification and fail to monitor real-time presence during lectures. This paper presents a Smart Classroom Monitoring System that uses a combination of IoT sensors and basic AI techniques to accurately detect faculty presence inside a classroom.
The system combines motion detection using a PIR sensor with identity verification methods such as face detection, voice activity, and mobile device detection. A Raspberry Pi is used as the core processing unit, which collects sensor data and applies a decision-making algorithm to determine whether the faculty is present or absent. If the system detects continuous absence beyond a defined time threshold, it automatically sends an alert to the concerned authority.
The proposed system is designed to work efficiently even in real-world classroom conditions where lighting, movement, and noise vary. By combining multiple detection methods, the system reduces false alerts and improves reliability. This approach provides a practical and cost-effective solution for automated classroom monitoring.