Malnutrition is a global health crisis and is the leading cause of death among children under five. Detecting malnutrition requires anthropometric measurements of weight, height, and middle-upper arm circumference. However, measuring them accurately is a challenge, especially in the global south, due to limited resources. In this work, we propose a CNN-based approach to estimate the height of standing children under five years from depth images collected using a smart-phone. According to the SMART Methodology Manual [5], the acceptable accuracy for height is less than 1.4 cm. On training our deep learning model on 87131 depth images, our model achieved an average mean absolute error of 1.64% on 57064 test images. For 70.3% test images, we estimated height accurately within the acceptable 1.4 cm range. Thus, our proposed solution can accurately detect stunting (low height-for-age) in standing children below five years of age.

中文翻译：

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使用深度图像估算5岁以下儿童的身高

营养不良是全球性的健康危机，是五岁以下儿童死亡的主要原因。要检测营养不良，需要对体重，身高和上臂中上围进行人体测量。但是，由于资源有限，准确测量它们是一个挑战，尤其是在全球南部。在这项工作中，我们提出了一种基于CNN的方法，可以根据使用智能手机收集的深度图像估算5岁以下站立儿童的身高。根据《 SMART方法论手册》 [5]，可接受的高度精度小于1.4厘米。通过在87131深度图像上训练我们的深度学习模型，我们的模型在57064测试图像上实现了1.64％的平均平均绝对误差。对于70.3％的测试图像，我们准确地估计了可接受的1.4厘米范围内的高度。因此，