Height Estimation of Children under Five Years using Depth Images

TL;DR

This paper presents a CNN-based method to estimate the height of children under five using depth images from smartphones, achieving accuracy suitable for detecting stunting in resource-limited settings.

Contribution

The study introduces a novel deep learning approach for height estimation from depth images, enabling accurate malnutrition screening in low-resource environments.

Findings

Achieved an average MAE of 1.64% on test images.

70.3% of estimates within the acceptable 1.4 cm accuracy.

Demonstrated potential for large-scale malnutrition screening.

Abstract

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.