Computational approach to body mass index estimation from dressed people in 3D space. Issue 7 (16th April 2020)

• Record Type: Journal Article
• Authors: Jiang, Min; Shang, Yuanyuan; Guo, Guodong
• Is Part Of: IET image processing. Volume 14:Issue 7(2020)
• Page Start: 1248
• Page End: 1256
• Publication Date: 2020-04-16
• Subjects: anthropometry -- image reconstruction -- clothing -- video signal processing -- image colour analysis -- medical computing; body mass index estimation -- BMI estimation -- three‐dimensional visual data -- estimated body volume -- body weight -- height estimation -- normally dressed people -- body volume estimation -- reconstructed 3D data -- estimated BMI -- BMI computation method -- RGB‐D video dataset -- KinectFusion; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1170
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Body mass index (BMI) defines as a person's weight divided by the square of height (BMI = ( weight ( lb ) / height ( in ) 2 ) × 703 ), which is an important indicator of the health condition. The authors study BMI estimation from the three‐dimensional (3D) visual data by measuring the correlation between the estimated body volume and BMIs, and then develop an efficient BMI computation method. Their approach consists of body weight and height estimation from normally dressed people in 3D space. To address the influence of loose clothes on body volume estimation, two clothes models are developed to make the volume estimation more accurate. A new RGB‐D video dataset is collected for this study, and the reconstructed 3D data are provided by the KinectFusion on depth data. Experimental results show the effectiveness of the approach to work on normal conditions of dressed people. The mean absolute error of the estimated BMI can achieve 2.54 in their experiments.