  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.
