IOT-BASED AUTOMATIC BMI MONITORING SYSTEM WITH RFID AND TRL EVALUATION - Rohmah, Ratnasari Nur; Aprilianto, Catur Putra; Budiman, Aris; Mubin, Mohammad Nasru; Wicaksono, Mokhamad Arfan; Nurokhim, Nurokhim (Jurnal Ilmiah Ilmu Terapan Universitas Jambi, 2026)
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.