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Erschienen in:
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2022 | OriginalPaper | Buchkapitel

Attributed Graphettes-Based Preterm Infants Motion Analysis

verfasst von : Davide Garbarino, Matteo Moro, Chiara Tacchino, Paolo Moretti, Maura Casadio, Francesca Odone, Annalisa Barla

Erschienen in: Complex Networks & Their Applications X

Verlag: Springer International Publishing

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Abstract

The study of preterm infants neuro-motor status can be performed by analyzing infants spontaneous movements. Nowadays, available automatic methods for assessing infants motion patterns are still limited. We present a novel pipeline for the characterization of infants spontaneous movements, which given RGB videos leverages on network analysis and NLP. First, we describe a body configuration for each frame considering landmark points on infants bodies as nodes of a network and connecting them depending on their proximity. Each configuration can be described by means of attributed graphettes. We identify each attributed graphette by a string, thus allowing to study videos as texts, i.e. sequences of strings. This allows us exploiting NLP methods as topic modelling to obtain interpretable representations. We analyze topics to describe both global and local differences in infants with normal and abnormal motion patters. We find encouraging correspondences between our results and evaluations performed by expert physicians.

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Metadaten
Titel
Attributed Graphettes-Based Preterm Infants Motion Analysis
verfasst von
Davide Garbarino
Matteo Moro
Chiara Tacchino
Paolo Moretti
Maura Casadio
Francesca Odone
Annalisa Barla
Copyright-Jahr
2022
Buch
Complex Networks & Their Applications X

Print ISBN: 978-3-030-93408-8

Electronic ISBN: 978-3-030-93409-5

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-030-93409-5_8

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