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Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis

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Intertwining Graphonomics with Human Movements (IGS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13424))

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Abstract

Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient’s functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognising patients’ movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients’ progress during the rehabilitation sessions that correspond to the clinicians’ findings about each case.

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Notes

  1. 1.

    http://dag.cvc.uab.es/patientmonitoring/.

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Acknowledgment

This work has been partially supported by the Spanish project RTI2018-095645-B-C21, the CERCA Program/Generalitat de Catalunya 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).

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Authors and Affiliations

  1. Computer Vision Center, Computer Science Department, Universitat Autònoma de Barcelona, Barcelona, Spain

    Asma Bensalah, Alicia Fornés & Josep Lladós

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

    Cristina Carmona-Duarte

Authors
  1. Asma Bensalah
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  2. Alicia Fornés
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  3. Cristina Carmona-Duarte
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  4. Josep Lladós
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Corresponding author

Correspondence to Asma Bensalah .

Editor information

Editors and Affiliations

  1. Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Cristina Carmona-Duarte

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

    Moises Diaz

  3. Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Miguel A. Ferrer

  4. Universidad Autónoma de Madrid, Madrid, Spain

    Aythami Morales

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Bensalah, A., Fornés, A., Carmona-Duarte, C., Lladós, J. (2022). Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis. In: Carmona-Duarte, C., Diaz, M., Ferrer, M.A., Morales, A. (eds) Intertwining Graphonomics with Human Movements. IGS 2022. Lecture Notes in Computer Science, vol 13424. Springer, Cham. https://doi.org/10.1007/978-3-031-19745-1_25

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  • DOI: https://doi.org/10.1007/978-3-031-19745-1_25

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  • Online ISBN: 978-3-031-19745-1

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