Skip to main content
SpringerLink
Log in

Attributed Graphettes-Based Preterm Infants Motion Analysis

  • Conference paper
  • First Online:
Complex Networks & Their Applications X (COMPLEX NETWORKS 2021)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1072))

Included in the following conference series:

  • 3723 Accesses

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.

D. Garbarino and M. Moro—Equally Contributed

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 349.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 449.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 449.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Adde, L., Helbostad, J.L., Jensenius, A.R., Taraldsen, G., Grunewaldt, K.H., Støen, R.: Early prediction of cerebral palsy by computer-based video analysis of general movements: a feasibility study. Dev. Med. Child Neurol. 52(8), 773–778 (2010)

    Article  Google Scholar 

  2. Ahmedt-Aristizabal, D., Denman, S., Nguyen, K., Sridharan, S., Dionisio, S., Fookes, C.: Understanding patients’ behavior: vision-based analysis of seizure disorders. IEEE J. Biomed. Health Inform. 23(6), 2583–2591 (2019)

    Article  Google Scholar 

  3. Alghamdi, R., Alfalqi, K.: A survey of topic modeling in text mining. International Journal of Advanced Computer Science and Applications (IJACSA), vol. 6, no. 1 (2015)

    Google Scholar 

  4. Allen, M.C.: Neurodevelopmental outcomes of preterm infants. Current Opinion Neurol. 21(2), 123–128 (2008)

    Article  Google Scholar 

  5. Bax, M., et al.: Proposed definition and classification of cerebral palsy, April 2005. Dev. Med. Child Neurol. 47(8), 571–576 (2005)

    Google Scholar 

  6. Bayley, N.: Bayley scales of infant and toddler development: administration manual. Harcourt assessment (2006)

    Google Scholar 

  7. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    Google Scholar 

  8. Carse, B., Meadows, B., Bowers, R., Rowe, P.: Affordable clinical gait analysis: an assessment of the marker tracking accuracy of a new low-cost optical 3d motion analysis system. Physiotherapy 99(4), 347–351 (2013)

    Article  Google Scholar 

  9. Chambers, C., et al.: Computer vision to automatically assess infant neuromotor risk. IEEE Trans. Neural Syst. Rehabil. Eng. 28(11), 2431–2442 (2020)

    Google Scholar 

  10. Colyer, S.L., Evans, M., Cosker, D.P., Salo, A.I.: A review of the evolution of vision-based motion analysis and the integration of advanced computer vision methods towards developing a markerless system. Sports Med.-open 4(1), 1–15 (2018)

    Article  Google Scholar 

  11. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  12. Desmarais, Y., Mottet, D., Slangen, P., Montesinos, P.: A review of 3d human pose estimation algorithms for markerless motion capture. arXiv preprint arXiv:2010.06449 (2020)

  13. Dimitrova, T., Petrovski, K., Kocarev, L.: Graphlets in multiplex networks. Sci. Rep. 10(1), 1–13 (2020)

    Google Scholar 

  14. Garello, L., et al.: A study of at-term and preterm infants’ motion based on markerless video analysis (2021)

    Google Scholar 

  15. Goldberg, Y.: Neural network methods for natural language processing. Synthesis Lect. Hum. Lang. Technol. 10(1), 1–309 (2017)

    Article  Google Scholar 

  16. Hasan, A., Chung, P.C., Hayes, W.: Graphettes: constant-time determination of graphlet and orbit identity including (possibly disconnected) graphlets up to size 8. PloS One 12(8), e0181570 (2017)

    Google Scholar 

  17. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  18. Hesse, N., Bodensteiner, C., Arens, M., Hofmann, U.G., Weinberger, R., Sebastian Schroeder, A.: Computer vision for medical infant motion analysis: State of the art and rgb-d data set. In: Proceedings of the ECCV (2018)

    Google Scholar 

  19. Long, Q., Jin, Y., Song, G., Li, Y., Lin, W.: Graph structural-topic neural network. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1065–1073 (2020)

    Google Scholar 

  20. Mathis, A., et al.: Deeplabcut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21(9), 1281 (2018)

    Google Scholar 

  21. Meinecke, L., Breitbach-Faller, N., Bartz, C., Damen, R., Rau, G., Disselhorst-Klug, C.: Movement analysis in the early detection of newborns at risk for developing spasticity due to infantile cerebral palsy. Hum. Mov. Sci. 25(2), 125–144 (2006)

    Article  Google Scholar 

  22. Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824–827 (2002)

    Article  Google Scholar 

  23. Mimno, D., Wallach, H., Talley, E., Leenders, M., McCallum, A.: Optimizing semantic coherence in topic models. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 262–272 (2011)

    Google Scholar 

  24. Pržulj, N., Corneil, D.G., Jurisica, I.: Modeling interactome: scale-free or geometric? Bioinformatics 20(18), 3508–3515 (2004)

    Article  Google Scholar 

  25. Röder, M., Both, A., Hinneburg, A.: Exploring the space of topic coherence measures. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 399–408 (2015)

    Google Scholar 

  26. Salton, G., Harman, D.: Information retrieval. In: Encyclopedia of Computer Science, pp. 858–863 (2003)

    Google Scholar 

  27. Tu, K., Li, J., Towsley, D., Braines, D., Turner, L.D.: gl2vec: Learning feature representation using graphlets for directed networks. In: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 216–221 (2019)

    Google Scholar 

  28. World-Health-Organization: Preterm birth. https://www.who.int/news-room/fact-sheets/detail/preterm-birth (2018)

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matteo Moro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chiara Tacchino
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paolo Moretti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Maura Casadio
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Francesca Odone
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Annalisa Barla
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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 1072. Springer, Cham. https://doi.org/10.1007/978-3-030-93409-5_8

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93408-8

  • Online ISBN: 978-3-030-93409-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation