BMI Prediction from Handwritten English Characters Using a Convolutional Neural Network

TL;DR

This paper introduces a CNN-based method to predict BMI from handwritten English characters, achieving high accuracy and filling a gap in handwriting analysis for health assessment.

Contribution

The study presents the first deep learning approach linking handwritten character analysis to BMI prediction, using a new dataset and demonstrating superior accuracy.

Findings

Achieved 99.92% accuracy in BMI prediction from handwriting

CNN models outperform traditional architectures in this task

Handwritten characters can effectively predict health-related metrics

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.