Height Estimation of Children under Five Years using Depth Images
Anusua Trivedi, Mohit Jain, Nikhil Kumar Gupta, Markus Hinsche,, Prashant Singh, Markus Matiaschek, Tristan Behrens, Mirco Militeri, Cameron, Birge, Shivangi Kaushik, Archisman Mohapatra, Rita Chatterjee, Rahul Dodhia,, Juan Lavista Ferres

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…
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Taxonomy
TopicsNutrition and Health in Aging · Body Composition Measurement Techniques · Child Nutrition and Water Access
