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International Conference on Complex Networks and Their Applications

COMPLEX NETWORKS 2021: Complex Networks & Their Applications X pp 82–93Cite as

Attributed Graphettes-Based Preterm Infants Motion Analysis

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Part of the Studies in Computational Intelligence book series (SCI,volume 1015)

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.

Keywords

  • Temporal networks
  • Attributed graphettes
  • Preterm infants motion
  • Markerless human motion analysis
  • Deep learning
  • Natural language processing
  • Latent dirichlet allocation

D. Garbarino and M. Moro—Equally Contributed

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  • DOI: 10.1007/978-3-030-93409-5_8
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Author information

Authors and Affiliations

  1. Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Genova, Italy

    Davide Garbarino, Matteo Moro, Maura Casadio, Francesca Odone & Annalisa Barla

  2. Machine Learning Genoa (MaLGa) Center, Genova, Italy

    Davide Garbarino, Matteo Moro, Francesca Odone & Annalisa Barla

  3. Istituto Giannina Gaslini, Genova, Italy

    Chiara Tacchino & Paolo Moretti

Authors
  1. Davide Garbarino
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  2. Matteo Moro
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  3. Chiara Tacchino
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  4. Paolo Moretti
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  5. Maura Casadio
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  6. Francesca Odone
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  7. Annalisa Barla
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Corresponding author

Correspondence to Matteo Moro .

Editor information

Editors and Affiliations

  1. Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Madrid, Madrid, Spain

    Rosa Maria Benito

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LIB, UFR Sciences et Techniques, University of Burgundy, Dijon, France

    Hocine Cherifi

  4. Grupo Interdisciplinar de Sistemas Complejos, Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Thomas J. Watson College of Engineering and Applied Science, Binghamton University, Binghamton, USA

    Luis M. Rocha

  6. Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Tarragona, Spain

    Marta Sales-Pardo

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Garbarino, D. et al. (2022). Attributed Graphettes-Based Preterm Infants Motion Analysis. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1015. Springer, Cham. https://doi.org/10.1007/978-3-030-93409-5_8

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

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-93409-5

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