Non-invasive solutions (no sensors nor markers) appear the most appealing for assessment of body movements and facial dynamics in order to predict Neurodevelopmental disorders (NDD) even in the first days of life. To this aim, recent advances in machine learning applied could be effectively exploited on visual data framing the children, but they suffer from the scarcity of annotated data for training the algorithms. In order to fill this gap, in this paper, a semi-automatic tool specifically designed for labelling videos of children in cribs is introduced. It consists of a Graphical User Interface allowing to select: 1) videos, or static images, to be processed and 2) the desired annotation goal achieved by state-of-the-art deep learning-based neural architectures.
An Advanced Tool for Semi-automatic Annotation for Early Screening of Neurodevelopmental Disorders
Bernava Giuseppe Massimo;Leo Marco;Carcagnì Pierluigi;Distante Cosimo
2022
Abstract
Non-invasive solutions (no sensors nor markers) appear the most appealing for assessment of body movements and facial dynamics in order to predict Neurodevelopmental disorders (NDD) even in the first days of life. To this aim, recent advances in machine learning applied could be effectively exploited on visual data framing the children, but they suffer from the scarcity of annotated data for training the algorithms. In order to fill this gap, in this paper, a semi-automatic tool specifically designed for labelling videos of children in cribs is introduced. It consists of a Graphical User Interface allowing to select: 1) videos, or static images, to be processed and 2) the desired annotation goal achieved by state-of-the-art deep learning-based neural architectures.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
