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

Glorious Musangi Mark, Pierre Bakunzibake, C. Mikeka
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Abstract

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 non-communicable 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%.
基于物联网的身体质量指数(BMI)预测模型设计
超重和肥胖已成为与心脏骤停、2型糖尿病、中风、高血压和其他非传染性疾病(NCD)等疾病相关的主要健康问题,也是全球死亡的主要风险,导致的死亡人数超过体重不足。身体质量指数(BMI)是用体重和身高来衡量一个人的营养状况。一直以来计算BMI的研究都是基于传统的人工方法,这既耗时又容易出错,而且它们不是基于云计算的。很少有系统整合了机器学习,但准确率很低。本研究提出了一个基于物联网的身体质量指数预测模型的设计和开发。该系统由用于计算的NodeMCU微控制器和内置的ESP8266 WiFi模块、用于测量体重的人体称重传感器、用于测量身高的HX711称重传感器放大器模块和用于测量身高的HC-SR04超声波传感器组成。数值显示在16x2 LCD上,并发送到ThingSpeak进行存储和分析。ThingSpeak集成了MATLAB机器学习,根据身高和体重的感官数据进行预测。本研究使用监督指数高斯过程回归算法来预测一个人是体重过轻、正常体重、超重还是肥胖。设计的基于物联网的BMI计算系统的准确率为99.18%,人均时间减少1.1%,而ML模型的准确率为98%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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