Malnutrition poses a significant threat to global health, resulting from an inadequate intake of essential nutrients that adversely impacts vital organs and overall bodily functioning. Periodic examinations and mass screenings, incorporating both conventional and non-invasive techniques, have been employed to combat this challenge. However, these approaches suffer from critical limitations, such as the need for additional equipment, lack of comprehensive feature representation, absence of suitable health indicators, and the unavailability of smartphone implementations for precise estimations of Body Fat Percentage (BFP), Basal Metabolic Rate (BMR), and Body Mass Index (BMI) to enable efficient smart-malnutrition monitoring. To address these constraints, this study presents a groundbreaking, scalable, and robust smart malnutrition-monitoring system that leverages a single full-body image of an individual to estimate height, weight, and other crucial health parameters within a multi-modal learning framework. Our proposed methodology involves the reconstruction of a highly precise 3D point cloud, from which 512-dimensional feature embeddings are extracted using a headless-3D classification network. Concurrently, facial and body embeddings are also extracted, and through the application of learnable parameters, these features are then utilized to estimate weight accurately. Furthermore, essential health metrics, including BMR, BFP, and BMI, are computed to conduct a comprehensive analysis of the subject's health, subsequently facilitating the provision of personalized nutrition plans. While being robust to a wide range of lighting conditions across multiple devices, our model achieves a low Mean Absolute Error (MAE) of $\pm$ 4.7 cm and $\pm$ 5.3 kg in estimating height and weight.

翻译：营养不良因必需营养素摄入不足而对全球健康构成严重威胁，进而损害重要器官及整体身体机能。为应对这一挑战，传统和非侵入性技术的定期检查与大规模筛查已被采用。然而，这些方法存在关键局限性，例如需要额外设备、缺乏全面的特征表示、缺少合适的健康指标，以及缺乏用于精确估计体脂百分比、基础代谢率和身体质量指数以实现高效智能营养不良监测的智能手机实施方案。为解决这些限制，本研究提出了一种开创性、可扩展且稳健的智能营养不良监测系统，该系统利用个体的单张全身图像，在多模态学习框架内估计身高、体重及其他关键健康参数。我们提出的方法涉及重建高精度三维点云，并通过无头三维分类网络从中提取512维特征嵌入。同时，还提取面部和身体嵌入，并通过应用可学习参数，利用这些特征准确估计体重。此外，计算包括BMR、BFP和BMI在内的基本健康指标，以对受试者的健康状况进行全面分析，随后提供个性化营养计划。该模型在对多种设备上的广泛光照条件具有稳健性的同时，在身高和体重估计中实现了±4.7厘米和±5.3千克的低平均绝对误差。