Design, Implementation, and Performance Evaluation of an IoT-Based Body Mass Index (BMI) Machine

Abstract

Obesity is the leading cause of cardiovascular death in developed and developing countries, the Conventional health monitoring relies on individual, manual assessments of weight and height, which frequently result in human error in Body Mass Index and logging delays. However, there is a need for the design and Implementation an accurate IoT-Based Body Mass Index (BMI) Machine.
The design, Implementation, and Performance Evaluation of an IoT-Based Body Mass Index (BMI) Machine based on the Internet of Things (IoT) are presented in this study. The designed system incorporates a load cell with a HX711 instrumentation amplifier for weight acquisition and an ultrasonic sensor (HC-SR04) for height measurement. After processing the physical inputs, an MCU ESP8266 microcontroller determines the user's BMI and categorizes their health status in accordance with World Health Organization (WHO) guidelines. For real-time mobile application tracking, data is shown locally on a 20x4 LCD screen and quickly transferred via Wi-Fi to a cloud database (Firebase). When compared to calibrated manual equipment, experimental validation on 55 test subjects showed great systemic accuracy with modest mean error rates of 0.64% for height and 1.50% for weight. This intelligent provides a medical dependability.