pith. sign in

arxiv: 2604.22697 · v1 · submitted 2026-04-24 · 💻 cs.CY · cs.HC

RFID-Based Non-Biometric Classroom Attendance System: Proxy Attendance Detection via Weight Sensor Integration

Furkan Ege , Muhsin \"Ozdemir This is my paper

Pith reviewed 2026-05-08 09:45 UTC · model grok-4.3

classification 💻 cs.CY cs.HC
keywords RFIDattendance systemproxy detectionweight sensornon-biometricIoTArduinoprivacy
0
0 comments X

The pith

A weight sensor paired with RFID cards can detect proxy attendance by checking against age-group statistics without storing any personal data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to replace time-consuming manual roll calls and easy card-swapping with an electronic system that still avoids the privacy problems of fingerprints or facial recognition. After a student scans an RFID card, the system weighs the person and compares the reading to a broad statistical range drawn from public data on 18-to-22-year-olds. Because no individual measurements or images are kept, the approach sidesteps GDPR, KVKK, and FERPA restrictions while making it harder for one student to sign in for another. A working prototype built from Arduino, Bluetooth, and a Python interface shows that the pieces can run together in classroom-like conditions. If the method works at scale, institutions could gain reliable attendance records at low cost and with minimal legal risk.

Core claim

The authors claim that adding a weight sensor to an RFID reader and comparing the live measurement against a non-personal statistical reference range for the 18-22 age group provides a practical safeguard against proxy attendance. The system records only the RFID identifier and a pass/fail weight check, stores no biometric templates, and uses off-the-shelf components to deliver attendance lists and CSV reports through a Bluetooth-linked GUI.

What carries the argument

The weight-verification step that compares the sensor reading to a statistical reference range compiled from three public Kaggle datasets of 350 individuals aged 18-22.

If this is right

  • The system reduces successful proxy attendance because the actual card holder must be present for the weight check to pass.
  • No personal biometric data is stored or transmitted, satisfying common data-protection regulations.
  • The prototype demonstrates that RFID reading, weight sensing, Bluetooth transfer, and GUI reporting can operate together without custom hardware.
  • The design is reproducible and low-cost, allowing other institutions to assemble similar setups from standard components.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same weight-check logic could be applied to other non-biometric access-control tasks where proxy use is a concern.
  • Collecting more public statistics or splitting the reference range by gender might tighten detection without changing the privacy model.
  • Repeated measurements over a semester could allow the system to flag unusual weight changes for the same RFID card while still avoiding stored personal profiles.

Load-bearing premise

A single broad weight range for the entire 18-22 age group will reliably flag most proxy attendees while producing few false alarms and resisting easy circumvention in normal classroom conditions.

What would settle it

A controlled trial in which several students whose weights fall inside the reference range swap RFID cards; if the system accepts every swapped card without flagging a proxy, the detection method does not work as claimed.

Figures

Figures reproduced from arXiv: 2604.22697 by Furkan Ege, Muhsin \"Ozdemir.

Figure 1
Figure 1. Figure 1: Fritzing circuit diagram of the system (Arduino UNO, RC522, HX711, HC-06, LCD). view at source ↗
Figure 2
Figure 2. Figure 2: Half-bridge load cell with a capacity of 50 kg. view at source ↗
Figure 3
Figure 3. Figure 3: HC-06 Bluetooth module. 3.2.5. 16×2 I2C LCD Display A 16×2 LCD display with I2C interface was used to provide instant feedback to students. Since the I2C protocol requires only two communication lines, the Arduino's pin usage is significantly reduced (HandsOn Tech., 2018) view at source ↗
Figure 4
Figure 4. Figure 4: 16×2 I2C LCD display view at source ↗
Figure 5
Figure 5. Figure 5: Flowchart of RFID card scanning and verification. view at source ↗
Figure 6
Figure 6. Figure 6: Flowchart of weight sensor verification. view at source ↗
Figure 7
Figure 7. Figure 7: Gender distribution of the sample (n=350; Male: 51.4%, Female: 48.6%). view at source ↗
Figure 8
Figure 8. Figure 8: Weight distributions by age and gender (18–22 age group, n=350). view at source ↗
Figure 9
Figure 9. Figure 9: Mean weight values by age group (18–22 age group, n=350). view at source ↗
Figure 10
Figure 10. Figure 10: HX711 ADC module with four soldered weight sensors. view at source ↗
Figure 11
Figure 11. Figure 11: RC522 RFID module connected with color-coded wires. view at source ↗
Figure 12
Figure 12. Figure 12: Overall view of the implemented prototype hardware. view at source ↗
Figure 13
Figure 13. Figure 13: Python GUI main screen: attendance records, student and course management view at source ↗
Figure 14
Figure 14. Figure 14: LCD display at system startup: "Sistem Hazir, Kartinizi Okutun" (English: "System view at source ↗
Figure 15
Figure 15. Figure 15: LCD display when a registered card is scanned: "HOS GELDINIZ" (English: view at source ↗
Figure 16
Figure 16. Figure 16: LCD display when an unregistered card is scanned: "Ogrenci Bulunama" (English: view at source ↗
Figure 17
Figure 17. Figure 17: "Yanlis Saat" warning (English: "Wrong Time") for scanning outside course hours. view at source ↗
Figure 18
Figure 18. Figure 18: Attendance records obtained on the GUI during real operation. Note: GUI labels are view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The manuscript proposes a non-biometric IoT attendance system combining RFID card reading with weight sensor verification to prevent proxy attendance in classrooms. The prototype uses an Arduino UNO, RFID module, load cells, Bluetooth, and a Python GUI for data management and reporting. Weight measurements are validated against a statistical range derived from 350 individuals aged 18-22 sourced from Kaggle datasets, without storing personal data. Qualitative integration tests under simulated classroom conditions are reported as successful.

Significance. The work addresses a practical problem in educational settings with a privacy-focused approach using off-the-shelf components, which is commendable for reproducibility and low cost. If the proxy detection mechanism were shown to be effective through rigorous testing, it could contribute to the field of secure, non-invasive attendance systems. However, the current evidence does not support the effectiveness claim.

major comments (3)
  1. [Abstract] The central claim that the system reduces proxy attendance is not supported by any quantitative data. The weight verification uses a broad demographic reference range, which would allow most plausible proxies to pass undetected since they would likely fall within the 18-22 age group weight distribution.
  2. [Weight Verification Component] No details are provided on how the reference range was computed (e.g., mean ± standard deviations, percentiles), the exact min/max values, or validation against actual student weights in the target population. This makes it impossible to evaluate the detection margin.
  3. [Results and Evaluation] The reported 'qualitative tests' only confirm module integration but include no error analysis for weight sensors, no false negative rates for proxy scenarios, and no discussion of environmental factors like sensor drift or clothing weight that could affect reliability.
minor comments (3)
  1. [Abstract] The abstract references 'three Kaggle datasets' without providing names, links, or DOIs, which is necessary for reproducibility.
  2. [System Architecture] A schematic diagram or wiring diagram of the Arduino connections would greatly improve clarity of the hardware integration.
  3. [Introduction] The manuscript would benefit from a discussion of related work on non-biometric attendance systems to better position the contribution.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for the constructive and detailed comments on our manuscript. We have addressed each major comment point by point below, making revisions to clarify limitations, provide additional methodological details, and tone down unsupported claims where necessary.

read point-by-point responses

  1. Referee: [Abstract] The central claim that the system reduces proxy attendance is not supported by any quantitative data. The weight verification uses a broad demographic reference range, which would allow most plausible proxies to pass undetected since they would likely fall within the 18-22 age group weight distribution.

    Authors: We agree that the original abstract overstated the system's impact by implying a reduction in proxy attendance without supporting quantitative evidence. The weight verification relies on a broad statistical range derived from Kaggle data for ages 18-22, which would indeed fail to detect proxies within the same demographic whose weights fall inside the range. In the revised manuscript, we have updated the abstract to state that the system 'aims to deter proxy attendance through an additional weight verification layer' and have added a new Limitations section explicitly discussing the constraints of using a general demographic range rather than individualized data. revision: yes

  2. Referee: [Weight Verification Component] No details are provided on how the reference range was computed (e.g., mean ± standard deviations, percentiles), the exact min/max values, or validation against actual student weights in the target population. This makes it impossible to evaluate the detection margin.

    Authors: The original submission omitted the precise computation method for the reference range. The range was computed by combining the three Kaggle datasets and taking the minimum and maximum weight values across all 350 individuals aged 18-22. We have now added this information, including the exact min/max values and a description of the aggregation process, to the Methods section of the revised manuscript. As noted, no validation against the specific target student population was conducted to preserve privacy by avoiding collection of personal biometric data; this has been clarified as a deliberate design choice. revision: yes

  3. Referee: [Results and Evaluation] The reported 'qualitative tests' only confirm module integration but include no error analysis for weight sensors, no false negative rates for proxy scenarios, and no discussion of environmental factors like sensor drift or clothing weight that could affect reliability.

    Authors: The evaluation presented is limited to qualitative integration tests under simulated classroom conditions, with no quantitative error analysis, proxy scenario testing, or discussion of factors such as sensor drift and clothing weight. We have revised the Results section to provide a more detailed account of the test conditions and have added a Limitations subsection addressing these issues, including the potential impact of environmental factors and the absence of measured false negative rates. We concur that rigorous quantitative evaluation would be required to substantiate effectiveness claims. revision: yes

standing simulated objections not resolved
  • Quantitative evaluation of proxy detection performance, including false negative rates under realistic proxy scenarios, as this would require new controlled experiments beyond the scope of the original prototype development.

Circularity Check

0 steps flagged

No circularity: system description uses external data and qualitative tests only

full rationale

The paper describes an IoT hardware prototype for attendance tracking. It compiles a weight reference range from three external Kaggle datasets (350 individuals aged 18-22) and performs only qualitative integration tests of RFID, weight sensor, Bluetooth, and GUI modules under classroom-like conditions. No equations, fitted parameters, predictions, or self-citations appear in the derivation chain. The central claim of integrated operation and proxy reduction intent rests on standard hardware behavior and public data, with no reduction by construction to the paper's own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard IoT hardware assumptions and the sufficiency of a broad age-group weight range for proxy detection; no new entities are postulated and no parameters are fitted by the authors themselves.

axioms (2)
  • domain assumption Weight sensor measurements remain accurate and repeatable under typical classroom conditions.
    Required for the verification step to function as described.
  • domain assumption The compiled statistical weight range from the three Kaggle datasets represents the target student population without significant bias.
    Used as the sole reference for weight verification.

pith-pipeline@v0.9.0 · 5565 in / 1309 out tokens · 30718 ms · 2026-05-08T09:45:16.914966+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

10 extracted references · 10 canonical work pages

  1. [1]

    F., & Yeleç, M

    https://www.kvkk.gov.tr/Icerik/6649/Personal-Data-Protection-Law Koçak, N. F., & Yeleç, M. O. (2017). Parmak izi okumaya dayalı hızlı yoklama sistemi [A fast attendance system based on fingerprint reading]. Turkish Informatics Foundation Journal of Computer Science and Engineering, 15(1). [in Turkish] Köhli, M., Weimar, J., Schmidt, S., Schmidt, F. P., La...

    work page doi:10.3390/s24092935 2017

  2. [2]

    başkasının yerine imza atma

    [in Turkish] Soewito, B., Gaol, F. L., Simanjuntak, E., & Gunawan, F. E. (2016). Smart mobile attendance system using voice recognition and fingerprint on smartphone. In 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA) (pp. 175–180). IEEE. https://doi.org/10.1109/ISITIA.2016.7828654 SparkFun. (2016). Load Cell Amplifier H...

    work page doi:10.1109/isitia.2016.7828654 2016

  3. [3]

    başkasının yerine imza atma

    GİRİŞ Eğitim, bireyin davranışında kendi yaşantısı yoluyla ve kasıtlı olarak istendik değişimler meydana getirme süreci olup eğitim kurumları, bu sürecin gerçekleştiği ve öğrencilere bilgi, beceri ile tutum kazandırılmaya çalışılan kurumlardır (Ertürk, 1997: 12). Bu sürecin vazgeçilmez bir parçası olan ders devam takibi, öğrencilerin akademik başarısıyla ...

    work page 1997

  4. [4]

    Bu bölümde söz konusu kategorilerdeki temsil edici çalışmalar ele alınmakta ve mevcut araştırma boşluğu ortaya konulmaktadır

    İLGİLİ ÇALIŞMALAR Elektronik yoklama sistemleri üzerine yürütülen araştırmalar, kullanılan kimlik doğrulama teknolojisine göre üç ana başlık altında ele alınabilir: biyometrik tabanlı sistemler, biyometrik olmayan sistemler ve hibrit yaklaşımlar. Bu bölümde söz konusu kategorilerdeki temsil edici çalışmalar ele alınmakta ve mevcut araştırma boşluğu ortaya...

    work page 2009

  5. [5]

    Hoş Geldiniz, [Ad Soyad]

    SİSTEM MİMARİSİ VE TASARIMI Bu bölümde geliştirilen sistemin tasarım kararları, bileşen seçimleri ve mimari yapısı sunulmaktadır. Bölüm 5'teki fiziksel montaj ve yazılım geliştirme sürecinden farklı olarak, bu bölüm sistemin teorik çerçevesini ve özgün vekâleten yoklama tespit mekanizmasının işleyiş mantığını ele almaktadır. 3.1. Genel Sistem Mimarisi Öne...

    work page 2014

  6. [6]

    kilo havuzu

    AĞIRLIK SENSÖRÜ DOĞRULAMASINA YÖNELİK VERİ ANALİZİ Bu bölümde, Bölüm 3'te tasarlanan ağırlık sensörü doğrulama mekanizmasının karar eşiklerini belirlemek amacıyla yürütülen istatistiksel analiz sunulmaktadır. Analiz, Bölüm 5'teki fiziksel uygulamadan ve Bölüm 6'daki test sürecinden bağımsız olarak, sistemin teorik doğrulama altyapısını oluşturmaktadır. 4....

    work page 2024

  7. [7]

    UYGULAMA VE PROTOTİP Bu bölümde, Bölüm 3'te tasarlanan sistemin fiziksel olarak hayata geçirilme süreci ayrıntılandırılmaktadır. Bölüm 3'teki tasarım kararlarından farklı olarak, bu bölüm donanımın gerçek montajını, bileşenler arasındaki kablolama bağlantılarını, yazılım geliştirme adımlarını ve veri akış yapısını ele almaktadır. 45 5.1. Donanım Kurulumu ...

    work page

  8. [8]

    Hoş Geldiniz

    TEST SÜRECİ VE BULGULAR Bu bölümde, Bölüm 5'te gerçekleştirilen prototip üzerinde yürütülen test süreci ve elde edilen bulgular aktarılmaktadır. Bölüm 5'in montaj ve geliştirme sürecini ele almasından farklı olarak bu bölüm, sistemin nasıl çalıştığını ve hangi sorunların nasıl giderildiğini belgeleyen test sonuçlarına odaklanmaktadır. 6.1. Test Yaklaşımı ...

    work page

  9. [9]

    TARTIŞMA 7.1. Önerilen Sistemin Mevcut Yaklaşımlarla Karşılaştırılması Bu çalışmada geliştirilen sistem, elektronik yoklama literatüründeki biyometrik veri zorunluluğu ve vekâleten yoklama açığı gibi iki köklü sorunu eş zamanlı olarak ele alan bir mimari önermektedir. Biyometrik tabanlı sistemler yüksek doğruluk oranları raporlamaktadır. Soewito vd. (2016...

    work page 2016

  10. [10]

    Sistemin özgün katkısı, kimlik doğrulama (RFID) ile fiziksel varlık doğrulama (ağırlık sensörü) görevlerini birbirinden bağımsız iki katmana ayırmasında yatmaktadır

    SONUÇ Bu çalışmada, elektronik yoklama alanındaki biyometrik veri zorunluluğu ve RFID tabanlı sistemlerdeki fiziksel varlık doğrulama açığı gibi iki köklü problemi eş zamanlı olarak gideren iki katmanlı bir IoT mimarisi geliştirilmiş ve prototip düzeyinde test edilmiştir. Sistemin özgün katkısı, kimlik doğrulama (RFID) ile fiziksel varlık doğrulama (ağırl...

    work page doi:10.1109/iccecome.2018.8658705 2018