Activity Recognition From Newborn Resuscitation Videos

by   Øyvind Meinich-Bache, et al.

Objective: Birth asphyxia is one of the leading causes of neonatal deaths. A key for survival is performing immediate and continuous quality newborn resuscitation. A dataset of recorded signals during newborn resuscitation, including videos, has been collected in Haydom, Tanzania, and the aim is to analyze the treatment and its effect on the newborn outcome. An important step is to generate timelines of relevant resuscitation activities, including ventilation, stimulation, suction, etc., during the resuscitation episodes. Methods: We propose a two-step deep neural network system, ORAA-net, utilizing low-quality video recordings of resuscitation episodes to do activity recognition during newborn resuscitation. The first step is to detect and track relevant objects using Convolutional Neural Networks (CNN) and post-processing, and the second step is to analyze the proposed activity regions from step 1 to do activity recognition using 3D CNNs. Results: The system recognized the activities newborn uncovered, stimulation, ventilation and suction with a mean precision of 77.67 Moreover, the accuracy of the estimated number of Health Care Providers (HCPs) present during the resuscitation episodes was 68.32 indicate that the proposed CNN-based two-step ORAAnet could be used for object detection and activity recognition in noisy low-quality newborn resuscitation videos. Significance: A thorough analysis of the effect the different resuscitation activities have on the newborn outcome could potentially allow us to optimize treatment guidelines, training, debriefing, and local quality improvement in newborn resuscitation.


page 1

page 2

page 4


Object Detection During Newborn Resuscitation Activities

Birth asphyxia is a major newborn mortality problem in low-resource coun...

Object and Text-guided Semantics for CNN-based Activity Recognition

Many previous methods have demonstrated the importance of considering se...

Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning

In the Muslim community, the prayer (i.e. Salat) is the second pillar of...

FG-SSA: Features Gradient-based Signals Selection Algorithm of Linear Complexity for Convolutional Neural Networks

Recently, many convolutional neural networks (CNNs) for classification b...

Improving state estimation through projection post-processing for activity recognition in football

The past decade has seen an increased interest in human activity recogni...

Impact of Three-Dimensional Video Scalability on Multi-View Activity Recognition using Deep Learning

Human activity recognition is one of the important research topics in co...

Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks

This thesis explore different approaches using Convolutional and Recurre...

Please sign up or login with your details

Forgot password? Click here to reset