用手写英文字母预测人体质量指数，准确率超99%。
BMI Prediction from Handwritten English Characters Using a Convolutional Neural Network
- 构建卷积神经网络分析手写体字符特征
- 在48人数据集上达到99.92%预测准确率
- 首次将手写分析与BMI预测结合，适合健康监测场景
人体质量指数（BMI）是评估健康状况最广泛使用的指标，与体脂水平密切相关，可预测潜在疾病风险。尽管已有研究利用深度学习从人脸图像等数据中估算BMI，但尚未有工作探索手写分析与BMI预测之间的关联。本文提出一种基于卷积神经网络（CNN）的方法，通过分析48名参与者书写的连笔小写字母，实现对BMI的预测。实验结果表明，该方法在测试集上取得了99.92%的准确率，优于AlexNet（99.69%）和InceptionV3（99.53%）等主流CNN架构，验证了手写特征在健康评估中的潜力。
原文摘要 · Abstract (English)
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.
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