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I Can’t Believe It’s Not Better: In-air Movement for Alzheimer Handwriting Synthetic Generation

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Graphonomics in Human Body Movement. Bridging Research and Practice from Motor Control to Handwriting Analysis and Recognition (IGS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14285))

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Abstract

During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based models. To alleviate this problem, some works resort to synthetic data generation. Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models. In this work, we combine the domain knowledge about the Alzheimer’s disease for handwriting and use it for a more guided data generation. Concretely, we have explored the use of in-air movements for synthetic data generation.

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Acknowledgment

This work has been partially supported by the Spanish project PID2021-126808OB-I00 (GRAIL) and the FI fellowship AGAUR 2020 FI-SDUR 00497 (with the support of the Secretaria d’Universitats i Recerca of the Generalitat de Catalunya and the Fons Social Europeu). The authors acknowledge the support of the Generalitat de Catalunya CERCA Program to CVC’s general activities.

Author information

Authors and Affiliations

  1. Computer Vision Center, Universitat Autònoma de Barcelona, Bellaterra, Spain

    Asma Bensalah, Alicia Fornés & Josep Lladós

  2. Computer Science Department, Universitat Autònoma de Barcelona, Bellaterra, Spain

    Asma Bensalah, Alicia Fornés & Josep Lladós

  3. DIEM, University of Salerno, Via Giovanni Paolo II 132, 84084, Fisciano, SA, Italy

    Antonio Parziale, Giuseppe De Gregorio & Angelo Marcelli

  4. AI3S Unit, CINI National Laboratory of Artificial Intelligence and Intelligent Systems, University of Salerno, Fisciano, SA, Italy

    Antonio Parziale, Giuseppe De Gregorio & Angelo Marcelli

Authors
  1. Asma Bensalah
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  2. Antonio Parziale
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  3. Giuseppe De Gregorio
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  4. Angelo Marcelli
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  5. Alicia Fornés
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  6. Josep Lladós
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Corresponding author

Correspondence to Asma Bensalah .

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Editors and Affiliations

  1. University of Salerno, Fisciano, Italy

    Antonio Parziale

  2. Universidad de las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Moises Diaz

  3. University of Lisbon, Lisbon, Portugal

    Filipe Melo

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Bensalah, A., Parziale, A., De Gregorio, G., Marcelli, A., Fornés, A., Lladós, J. (2023). I Can’t Believe It’s Not Better: In-air Movement for Alzheimer Handwriting Synthetic Generation. In: Parziale, A., Diaz, M., Melo, F. (eds) Graphonomics in Human Body Movement. Bridging Research and Practice from Motor Control to Handwriting Analysis and Recognition. IGS 2023. Lecture Notes in Computer Science, vol 14285. Springer, Cham. https://doi.org/10.1007/978-3-031-45461-5_10

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  • DOI: https://doi.org/10.1007/978-3-031-45461-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45460-8

  • Online ISBN: 978-3-031-45461-5

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