Generalized vs Specialized activity recognition system for newborn resuscitation videos using Deep Neural Networks.

Abstract

Birth asphyxia is a global problem which has resulted in a high mortality rate of newborn babies all over the globe, it is a newborn’s inability to establish breathing at birth. A notable breakthrough is the marrying of medical technology with information technology in an attempt to tackle this global health problem. An example of this is the Safer Births project which is focused on establishing technological advancement to curb newborn deaths. In the year 2013, the Safer Births project started and has till date gathered a lot of data captured during resuscitation sessions. The Haydom data used for the Safer Births project and additional data from Nepal and SUS will be used with the aim of comparing a specialized and generalized model trained on activity recognition system I3D and RGB stream excluding optical flow. With focus on only the newborn region, the reason for this is to simplify the existing model. The experiment was conducted in view of the possibility of achieving a system that can generalize or specialize with a combination of different hospital data on some specific activities of interest namely Ventilation, Suction, Stimulation. A new simplified pipeline, which is a reduction of the previous work done by the saferbirth group, showed a very poor performance when generalized.