NurtureNet: A Multi-task Video-based Approach for Newborn Anthropometry

TL;DR

NurtureNet is a multi-task deep learning model that uses low-cost smartphone videos and tabular data to accurately estimate newborn anthropometry, aiding health workers in resource-limited settings.

Contribution

The paper introduces NurtureNet, a novel multi-task model combining visual and tabular data for contactless newborn measurements in rural communities.

Findings

Achieves 3.9% relative error in weight estimation

Model compressed to 15 MB for offline smartphone deployment

Visual proxy tasks improve anthropometry prediction accuracy

Abstract

Malnutrition among newborns is a top public health concern in developing countries. Identification and subsequent growth monitoring are key to successful interventions. However, this is challenging in rural communities where health systems tend to be inaccessible and under-equipped, with poor adherence to protocol. Our goal is to equip health workers and public health systems with a solution for contactless newborn anthropometry in the community. We propose NurtureNet, a multi-task model that fuses visual information (a video taken with a low-cost smartphone) with tabular inputs to regress multiple anthropometry estimates including weight, length, head circumference, and chest circumference. We show that visual proxy tasks of segmentation and keypoint prediction further improve performance. We establish the efficacy of the model through several experiments and achieve a relative error of 3.9% and mean absolute error of 114.3 g for weight estimation. Model compression to 15 MB also allows offline deployment to low-cost smartphones.