Body Composition Estimation Based on Multimodal Multi-task Deep Neural Network

TL;DR

This paper presents a multimodal multi-task deep neural network that estimates body composition, specifically body fat percentage and skeletal muscle mass, using facial images along with demographic data, outperforming existing methods.

Contribution

The study introduces a novel multi-task deep learning model that combines facial images and demographic data for accurate body composition estimation, capturing the correlation between fat and muscle mass.

Findings

The proposed model outperforms existing methods in body composition estimation.

It effectively captures the negative correlation between body fat and muscle mass.

The approach demonstrates improved accuracy on a Japanese demographic dataset.

Abstract

In addition to body weight and Body Mass Index (BMI), body composition is an essential data point that allows people to understand their overall health and body fitness. However, body composition is largely made up of muscle, fat, bones, and water, which makes estimation not as easy and straightforward as measuring body weight. In this paper, we introduce a multimodal multi-task deep neural network to estimate body fat percentage and skeletal muscle mass by analyzing facial images in addition to a person's height, gender, age, and weight information. Using a dataset representative of demographics in Japan, we confirmed that the proposed approach performed better compared to the existing methods. Moreover, the multi-task approach implemented in this study is also able to grasp the negative correlation between body fat percentage and skeletal muscle mass gain/loss.