Artificial intelligence tools for engagement prediction in neuromotor disorder patients during rehabilitation.

Journal: Journal of neuroengineering and rehabilitation
PMID: 39702317

Abstract

BACKGROUND: Robot-Assisted Gait Rehabilitation (RAGR) is an established clinical practice to encourage neuroplasticity in patients with neuromotor disorders. Nevertheless, tasks repetition imposed by robots may induce boredom, affecting clinical outcomes. Thus, quantitative assessment of engagement towards rehabilitation using physiological data and subjective evaluations is increasingly becoming vital. This study aimed at methodologically exploring the performance of artificial intelligence (AI) algorithms applied to structured datasets made of heart rate variability (HRV) and electrodermal activity (EDA) features to predict the level of patient engagement during RAGR.

Authors

  • Simone Costantini
    Department of Electronics Information and Bioengineering, Politecnico di Milano, Milan, Italy. simone.costantini@polimi.it.
  • Anna Falivene
    Scientific Institute, IRCCS "E. Medea", Bosisio Parini, Italy.
  • Mattia Chiappini
    Scientific Institute, IRCCS "E. Medea", Bosisio Parini, Italy.
  • Giorgia Malerba
    Scientific Institute, IRCCS "E. Medea", Bosisio Parini, Italy.
  • Carla Dei
    Scientific Institute, IRCCS "E. Medea", Bosisio Parini, Italy.
  • Silvia Bellazzecca
    Scientific Institute, IRCCS "E. Medea", Bosisio Parini, Italy.
  • Fabio A Storm
    Scientific Institute, IRCCS "E. Medea", Bosisio Parini, Italy.
  • Giuseppe Andreoni
    Mechanical Engineering Department, Politecnico di Milano, Via Giuseppe La Masa 1, 20156 Milan, Italy.
  • Emilia Ambrosini
  • Emilia Biffi
    Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Italy.

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