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BMIMatic: Body mass index derivation from captured images
Author(s): Adomar L. Ilao; Adrian Christopher Cardino; Clarence Fernandez; Lawrence Saulon
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Paper Abstract

Body Mass Index (BMI) is a biometric trait in which it can determine the malnutrition status of a person. Studies from the past incorporated computer vision in order to determine the height and weight of the person. This study’s purpose is to obtain the height, weight and BMI of the person through computer vision. The captured image undergoes image segmentation to derive height and weight. The derived medical parameters will be used to determine Body Mass Index as a basis of the malnutrition status of the subject. The validity of the derived values had been verified through statistical tools such as Percent Accuracy, One–Way ANOVA, T- Test, Pearson Correlation and Scheffe Test. The statistical tools shown derived height yielded 93.9% accurate. While derived weight through Body Surface Area and Linear Regression resulted 66.6% and 80.14% accurate respectively. Furthermore, derived BMI for both BSA and Linear Regression, came out 66.3% and 84.9% accurate respectively.

Paper Details

Date Published: 14 August 2019
PDF: 10 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111790J (14 August 2019); doi: 10.1117/12.2540197
Show Author Affiliations
Adomar L. Ilao, Malayan Colleges Laguna (Philippines)
Adrian Christopher Cardino, Malayan Colleges Laguna (Philippines)
Clarence Fernandez, Malayan Colleges Laguna (Philippines)
Lawrence Saulon, Malayan Colleges Laguna (Philippines)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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