# Height Estimation of Children under Five Years using Depth Images

arXiv: https://arxiv.org/abs/2105.01688 · HTML: https://mlsanity.com/paper/2105.01688/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 2 of 2)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud](https://mlsanity.com/paper/1905.08705/) (2019)

## Built on by (most cited first, 1 of 1)

- [NurtureNet: A Multi-task Video-based Approach for Newborn Anthropometry](https://mlsanity.com/paper/2405.05530/) (2024)

## Abstract (arXiv)

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.