IOT-BASED AUTOMATIC BMI MONITORING SYSTEM WITH RFID AND TRL EVALUATION

Ratnasari Nur Rohmah; Catur Putra Aprilianto; Aris Budiman; Mohammad Nasru Mubin; Mokhamad Arfan Wicaksono; Nurokhim Nurokhim

doi:10.22437/jiituj.v10i1.53197

Authors

Ratnasari Nur Rohmah Universitas Muhammadiyah Surakarta https://orcid.org/0000-0003-1136-1314
Catur Putra Aprilianto Universitas Muhammadiyah Surakarta https://orcid.org/0009-0001-6087-8443
Aris Budiman Universitas Muhammadiyah Surakarta https://orcid.org/0009-0001-1068-718X
Mohammad Nasru Mubin Universitas Muhammadiyah Surakarta https://orcid.org/0009-0003-5717-1361
Mokhamad Arfan Wicaksono Bandung Digital Academy https://orcid.org/0000-0001-6563-1813
Nurokhim Nurokhim National Research and Innovation Agency (BRIN) https://orcid.org/0000-0001-7129-3005

DOI:

https://doi.org/10.22437/jiituj.v10i1.53197

Keywords:

BMI Monitoring, Cloud-based Health System, IoT Healthcare, RFID Authentication, Technology Readiness, Usability Evaluation

Abstract

The increasing global prevalence of overweight and obesity, combined with limitations in conventional Body Mass Index (BMI) measurement practices, underscores the need for secure, automated, and cloud-integrated monitoring systems. Existing IoT-based BMI devices primarily focus on measurement accuracy but rarely integrate secure user authentication, longitudinal cloud tracking, usability validation, and formal technology readiness assessment within a single framework. This study aims to design, implement, and evaluate an IoT-based automatic BMI monitoring system equipped with RFID authentication and real-time cloud synchronization. The proposed system integrates an ESP32 microcontroller, ultrasonic and load-cell sensors, and an RFID PN532 module. Measurement data are transmitted to a Firebase Realtime Database and visualized via a web-based dashboard. Accuracy was evaluated using mean error analysis and linear regression (R²). Usability was assessed using the System Usability Scale (SUS) and End-User Satisfaction (ESU) questionnaire. Technology maturity was analyzed using the Technology Readiness Level (TRL) framework. Experimental testing with 25 participants demonstrated high measurement accuracy, with mean errors of 0.38% for height (R² = 0.952) and 0.35% for weight (R² = 0.9993). BMI computation showed strong agreement with manual calculation (R² = 0.9938). The average measurement cycle required 15.2 seconds. The system achieved a SUS score of 82.5 (Excellent), ESU score of 4.6/5 (Very Satisfied), and TRL 6 classification. The novelty of this study lies in integrating secure RFID authentication, cloud-based longitudinal monitoring, dual-layer usability evaluation, and formal TRL assessment into a single IoT BMI ecosystem.

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Author Biographies

Ratnasari Nur Rohmah, Universitas Muhammadiyah Surakarta

Department of Electrical Engineering, Universitas Muhammadiyah Surakarta, Jawa Tengah, Indonesia

Catur Putra Aprilianto, Universitas Muhammadiyah Surakarta

Department of Electrical Engineering, Universitas Muhammadiyah Surakarta, Jawa Tengah, Indonesia

Aris Budiman, Universitas Muhammadiyah Surakarta

Department of Electrical Engineering, Universitas Muhammadiyah Surakarta, Jawa Tengah, Indonesia

Mohammad Nasru Mubin, Universitas Muhammadiyah Surakarta

Department of Electrical Engineering, Universitas Muhammadiyah Surakarta, Jawa Tengah, Indonesia

Mokhamad Arfan Wicaksono, Bandung Digital Academy

Software Engineering Technology Department, Akademi Digital Bandung, Jawa Barat, Indonesia

Nurokhim Nurokhim, National Research and Innovation Agency (BRIN)

Research Organisation for Nuclear Energy (ORTN), National Research and Innovation Agency (BRIN), Banten, Indonesia

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