ABSTRACT
Malnutrition is a global health crisis and is a leading cause of death among children under 5 years. 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 5 years from depth images collected using a smartphone. According to the SMART Methodology Manual, the acceptable accuracy for height is less than 1.4 cm. On training our deep learning model on 87131 depth images, our model achieved a 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 5 years of age.
