论文标题
基于多模式多任务深神经网络的身体组成估计
Body Composition Estimation Based on Multimodal Multi-task Deep Neural Network
论文作者
论文摘要
除了体重和体重指数（BMI）外，身体成分是一个必不可少的数据点，使人们能够了解其整体健康和身体健康。但是，身体成分在很大程度上由肌肉，脂肪，骨骼和水组成，这使得估计不像测量体重那么容易且直接。在本文中，我们引入了一个多模式的多任务深神经网络，除了一个人的身高，性别，年龄和体重信息外，还通过分析面部图像来估计身体脂肪百分比和骨骼肌质量。使用日本人口统计数据的数据集代表，我们证实，与现有方法相比，提出的方法的性能更好。此外，本研究中实施的多任务方法还能够掌握体内脂肪百分比与骨骼肌质量增益/损失之间的负相关性。
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.
