Abstract
Abstract—Nutrition is a significant determinant of health,
the resolution of many nutritional issues, initially requires an
anthropometry examination. Body measures provide data for
studying the relationship between diet, nutritional status, and
health. Manual and automatic methods can perform body measurements. The manual method usually uses an anthropometric
tape. However, the automatic process uses the equipment of
Dual-energy X-ray absorptiometry (DXA). Our work presents
a new approach to calculate body measures using 2D Camera
Images, applying Digital Image Processing, Convolution Neural
Networks, and Machine Learning techniques. The dataset used
contains 38 exams, for each exam, has four digital images
and the dimensions of body measurements, performed by a
specialist. The methods used in this work for segmentation were
Dense Human Pose Estimation - CNN with the Bayesian, KNearest Neighbors, Support Vector Machine, Decision Threes,
Adaptive Boosting, Random Forest, Multilayer Perceptron and
Expectation-Maximization classifiers. The approach with Dense
Human Pose Estimation and Expectation-Maximization reached
the best results, with mean squared error (MSE) always bellow
4.606 ± 3.412 cm when compared with specialist measures.
Index Terms—Nutrition, Body measures, Convolutional Neural
Networks, Machine Learning.
Modules
Algorithms
Software And Hardware
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL