Development of a Non-Contact Sensor System for Converting 2D Images into 3D Body Data: A Deep Learning Approach to Monitor Obesity and Body Shape in Individuals in Their 20s and 30s
Author:
Lee Ji-Yong1, Kwon Kihyeon2ORCID, Kim Changgyun3, Youm Sekyoung4
Affiliation:
1. Center for Sports and Performance Analysis, Korea National Sport University, Songpa-gu, Seoul 05541, Republic of Korea 2. Department of Information & Communication Engineering, Kangwon National University, Samcheok 25913, Gangwon-do, Republic of Korea 3. Department of Artificial Intelligence & Software, Kangwon National University, Samcheok 25913, Gangwon-do, Republic of Korea 4. Department of Industrial and Systems Engineering, Dongguk University, Seoul 04620, Republic of Korea
Abstract
This study demonstrates how to generate a three-dimensional (3D) body model through a small number of images and derive body values similar to the actual values using generated 3D body data. In this study, a 3D body model that can be used for body type diagnosis was developed using two full-body pictures of the front and side taken with a mobile phone. For data training, 400 3D body datasets (male: 200, female: 200) provided by Size Korea were used, and four models, i.e., 3D recurrent reconstruction neural network, point cloud generative adversarial network, skinned multi-person linear model, and pixel-aligned impact function for high-resolution 3D human digitization, were used. The models proposed in this study were analyzed and compared. A total of 10 men and women were analyzed, and their corresponding 3D models were verified by comparing 3D body data derived from 2D image inputs with those obtained using a body scanner. The model was verified through the difference between 3D data derived from the 2D image and those derived using an actual body scanner. Unlike the 3D generation models that could not be used to derive the body values in this study, the proposed model was successfully used to derive various body values, indicating that this model can be implemented to identify various body types and monitor obesity in the future.
Funder
National Research Foundation of Korea Ministry of Education
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference38 articles.
1. Tahrani, A., Boelaert, K., Barnes, R., Palin, S., Field, A., Redmayne, H., Aytok, L., and Rahim, A. (2008). Endocrine Abstracts, Bioscientifica. 2. Belarmino, G., Torrinhas, R.S., Sala, P., Horie, L.M., Damiani, L., Lopes, N.C., Heymsfield, S.B., and Waitzberg, D.L. (2018). A new anthropometric index for body fat estimation in patients with severe obesity. BMC Obes., 5. 3. Revision data 3 law and Issues of insurance business-Focusing on the activation of digital healthcare services;Kim;Korea Insur. Law J.,2020 4. Kim, J., Lee, J., and Park, M. (2022). Identification of smartwatch-collected lifelog variables affecting body mass index in middle-aged people using regression machine learning algorithms and SHapley Additive Explanations. Appl. Sci., 12. 5. Digital Innovation in Neuroanatomy: Three-Dimensional (3D) Image Processing and Printing for Medical Curricula and Health Care;Hurley;J. Neuropsychiatry Clin. Neurosci.,2023
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