Design of an IoT-based Body Mass Index (BMI) Prediction Model

Overweight and obesity have become a major health concern associated with diseases such as cardiac arrest, type 2 diabetes, stroke, high blood pressure, and other noncommunicable diseases (NCD) and are the leading risks for deaths globally, killing more people than underweight. Body Mass Index (BMI) is a measure that uses weight and height to work out a person's nutrition status. Research throughout to calculate BMI is based on traditional manual methods which are time consuming, error prone and they are not cloud-based. Very few systems have incorporated machine learning yet with low accuracy. This research presents the design and development of a IoT based body mass index prediction model. This system consists of a NodeMCU microcontroller for computations with an inbuilt ESP8266 WiFi module, human load cell sensor for body weight measurement, a HX711 load cell amplifier module and HC-SR04 ultrasonic sensor for height measurement. Values are displayed on a 16x2 LCD and send to ThingSpeak for storage and analysis. ThingSpeak is integrated with MATLAB Machine Learning to make the prediction based on height and weight sensory data. This research uses Supervised Exponential Gaussian Process Regression algorithm to predict whether a person is underweighted, normal weight, overweight or obese. The designed IoT Based BMI computation system achieves an accuracy of 99.18% with a time reduction of 1.1 % per person while the ML model achieves an accuracy of 98%.