RFID-Based Non-Biometric Classroom Attendance System: Proxy Attendance Detection via Weight Sensor Integration
2026-04-24 • Computers and Society
Computers and SocietyHuman-Computer Interaction
AI summaryⓘ
The authors identify problems with traditional attendance tracking that wastes class time and allows students to fake attendance. They note biometric systems raise privacy concerns and RFID cards alone can be easily shared. To fix this, they created a low-cost system combining RFID cards with weight sensors to check if the right student is present, without storing personal biometric data. They tested their system in a classroom-like setting and found it works well for tracking attendance and reducing fake sign-ins.
Attendance trackingBiometric authenticationRFIDProxy attendanceGDPRIoTWeight sensorsArduino UNOBluetooth communicationPrivacy laws
Authors
Furkan Ege, Muhsin Özdemir
Abstract
Attendance tracking in educational institutions, when conducted through traditional methods, leads to structural problems that consume instruction time and threaten academic integrity. Attendance durations spanning several minutes in primary and secondary education and exceeding ten minutes in higher education, combined with the proxy attendance problem of signing on behalf of someone else, demonstrate the need for electronic systems. Most existing electronic solutions rely on biometric authentication, which raises legal and ethical risks under the European General Data Protection Regulation (GDPR), the Turkish Personal Data Protection Law (KVKK), and the United States Family Educational Rights and Privacy Act (FERPA). Systems using RFID alone provide no built-in safeguard against proxy attendance through card transfer. This study proposes a biometric-free IoT attendance system addressing both deficiencies. The prototype consists of an RFID module, RFID cards, weight sensors, a Bluetooth module, and an Arduino UNO microcontroller. After the student presents their RFID card, the weight sensor measurement is compared against a statistical reference range of 350 individuals (aged 18-22) compiled from three Kaggle datasets; no personal biometric data is recorded. A Python-based GUI performs student management, course tracking, and CSV-based reporting via Bluetooth. Qualitative tests in conditions close to a real classroom have shown that the RFID reading, weight verification, Bluetooth communication, and GUI modules operate in an integrated manner as expected. The proposed system offers a low-cost and reproducible solution that aims to reduce proxy attendance without storing biometric data.