Open AccessComputational approach to body mass index estimation from dressed people in 3D space
Author(s) -
Jiang Min,
Shang Yuanyuan,
Guo Guodong
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.1170
Subject(s) - body mass index , estimation , volume (thermodynamics) , computation , computer science , mean squared error , rgb color model , index (typography) , statistics , artificial intelligence , computer vision , mathematics , algorithm , medicine , engineering , physics , systems engineering , pathology , quantum mechanics , world wide web
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.