Year: 2024
Type: preprint
Abstract: A person's Body Mass Index, or BMI, is the most widely used parameter for assessing their health. BMI is a crucial predictor of potential diseases that may arise at higher body fat levels because it is correlated with body fat. Conversely, a community's or an individual's nutritional status can be determined using the BMI. Although deep learning models are used in several studies to estimate BMI from face photos and other data, no previous research established a clear connection between deep learning techniques for handwriting analysis and BMI prediction. This article addresses this research gap with a deep learning approach to estimating BMI from handwritten characters by developing a convolutional neural network (CNN). A dataset containing samples from 48 people in lowercase English scripts is successfully captured for the BMI prediction task. The proposed CNN-based approach reports a commendable accuracy of 99.92%. Performance comparison with other popular CNN architectures reveals that AlexNet and InceptionV3 achieve the second and third-best performance, with the accuracy of 99.69% and 99.53%, respectively.
Source: arXiv (Cornell University)
Authors: Diba, N. T., Akter, N., Chowdhury, S. A. H., Giti, J. E.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403160215