Monitoring Ideal Body Mass Index Using a Blynk-Based Internet of Things Measurement System

Abstract

This study presents the design and implementation of an Internet of Things (IoT)-based automated measurement system for monitoring height, weight, and Body Mass Index (BMI). The system utilizes an ultrasonic sensor for height measurement and a load cell sensor for weight measurement, integrated with a NodeMCU ESP8266 microcontroller and the Blynk application for real-time data visualization. The increasing prevalence of obesity and lifestyle-related health issues, particularly during the COVID-19 pandemic, highlights the need for efficient and accurate health monitoring tools. The study adopts an action research methodology, consisting of diagnosing, action planning, action taking, evaluating, and learning phases. Experimental results show that weight measurements obtained from the load cell sensor produced a high level of accuracy with an average error of 0.5–0.75 kg, while height measurements using the ultrasonic sensor showed an average error of 1 cm. The system successfully displays measurement results on both an LCD module and the Blynk mobile application. The findings indicate that IoT-based measurement systems offer significant potential to support personal health monitoring and provide a practical solution for continuous tracking of BMI.