<![CDATA[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.]]>
