BMIMatic: Body mass index derivation from captured images

Adomar L. Ilao; Adrian Christopher Cardino; Clarence Fernandez; Lawrence Saulon

doi:10.1117/12.2540197

14 August 2019 BMIMatic: Body mass index derivation from captured images

Adomar L. Ilao, Adrian Christopher Cardino, Clarence Fernandez, Lawrence Saulon

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111790J (2019) https://doi.org/10.1117/12.2540197
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

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.

Citation Download Citation

Adomar L. Ilao, Adrian Christopher Cardino, Clarence Fernandez, and Lawrence Saulon "BMIMatic: Body mass index derivation from captured images", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111790J (14 August 2019); https://doi.org/10.1117/12.2540197

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