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

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.

Smart Classroom, IoT, Raspberry Pi, Face Detection, Automation, Monitoring System

1. Biondi, P. (n.d.). Scapy Documentation - Packet Manipulation. Retrieved from https://scapy.readthedocs.io/ [Google Scholar] [Crossref]

2. Bradski, G. (2000). The OpenCV Library. Dr. Dobb's Journal of Software Tools. [Google Scholar] [Crossref]

3. Grinberg, M. (2018). Flask Web Development: Developing Web Applications with Python. O'Reilly Media. [Google Scholar] [Crossref]

4. H. Rahman et al., “An Embedded Intelligent System for Attendance Monitoring,” arXiv preprint arXiv:2406.13694, 2024. [Online]. Available: https://arxiv.org/abs/2406.13694 [Google Scholar] [Crossref]

5. Hubert, M. (n.d.). PyAudio Documentation. Retrieved from https://people.csail.mit.edu/hubert/pyaudio/docs/ [Google Scholar] [Crossref]

6. M. Joshi et al., “IoT Based Class Monitoring System with Smart Attendance,” International Journal of Scientific Research in Engineering and Management, 2024. [Online]. Available: https://ijsrem.com [Google Scholar] [Crossref]

7. P. N. Reddy et al., “IoT Based Attendance Monitoring System Using Facial Recognition,” in Proceedings of IEEE Conference on Smart Technologies, 2023. [Online]. Available: ResearchGate [Google Scholar] [Crossref]

8. Python Software Foundation. (n.d.). Python Language Reference, version 3.x. Retrieved from https://docs.python.org/3/ [Google Scholar] [Crossref]

9. R. Kumar and S. Sharma, “Attendance Monitoring System Using Face Recognition,” International Journal of Innovative Technology and Research, 2022. DOI: 10.5281/zenodo.7385439 [Google Scholar] [Crossref]

10. Raspberry Pi Foundation. (n.d.). Raspberry Pi Documentation. Retrieved from https://www.raspberrypi.com/documentation/ [Google Scholar] [Crossref]

11. V. Sharma and A. Gupta, “Wi-Fi Based Smart Attendance Monitoring System,” 2023. [Online]. Available: ResearchGate [Google Scholar] [Crossref]

12. Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. Accepted Conference on Computer Vision and Pattern Recognition (CVPR). [Google Scholar] [Crossref]

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