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.
Abstract (translated)
一个人的身体质量指数（BMI）是对其健康状况最广泛使用的参数。 BMI 与身体脂肪水平相关，是预测在较高身体脂肪水平可能出现的潜在疾病的關鍵預測因素。相反，一个社区或个人的營養狀況可以使用 BMI 來確定。雖然在多個研究中，使用面照片和其他數據來估計 BMI 的深度學習模型，但之前的研究沒有建立手寫字分析中深度學習技術與 BMI 預測之間的明確聯繫。本文通過使用深度學習方法估計手寫字中的 BMI，來填补這個研究缺口。開發了一個卷積神經網絡（CNN），通過估計手寫字中的 BMI，成功捕捉了包含48個人的小寫英文字母數據集的 BMI 預測任務數據集。與其他流行的 CNN 架構相比，基於 CNN 的方法報告了 99.92% 的優秀準確度。性能比較顯示，AlexNet 和 InceptionV3 實現了第二和第三好的性能，分別達到 99.69% 和 99.53%。
URL
https://arxiv.org/abs/2409.02584
