Estimating anthropometric measurements through 2D images using machine learning with gender-based analysis

Anthropometric measurements are used in several fields of study and can be used in public health as an indicator of cardiovascular risk; they can also be used as a parameter for making tailored clothes or even for reconstructing the body for nutritional monitoring purposes. Thus, the automatic estimation of these measurements can improve anthropometric processes, even more so if it is based on 2D images, as this represents a gain due to the low cost of implementing this technology. Our work presents an approach to estimating these anthropometric measurements through images using Machine Learning. Furthermore, in this work, we propose a dataset containing 913 samples with images and measurements. Finally, we performed experiments to analyze the influence of patient information on the estimation of measurements, as well as a gender analysis to get insights from the dataset. In our approach, with specific configurations, we reached the mark of 0.778 ± 0.083 cm using MAE, 1.096 ± 0.292 cm using RMSE, and 2% using MAPE, differing on the experiment, gender, and classifier.