• DocumentCode
    3713441
  • Title

    Reinforcement-induced movement therapy: A novel approach for overcoming learned non-use in chronic stroke patients

  • Author

    Bel?n Rubio Ballester;Martina Maier;Rosa San Segundo;Victoria Casta?eda Galeano;Armin Duff;Paul F.M.J. Verschure

  • Author_Institution
    Laboratory of Synthetic Perceptive, Emotive, and Cognitive Systems, Universitat Pompeu Fabra, Barcelona Spain
  • fYear
    2015
  • fDate
    6/1/2015 12:00:00 AM
  • Firstpage
    183
  • Lastpage
    190
  • Abstract
    An open question in stroke rehabilitation is, if and how chronic patients can still make improvements after they reached a plateau in motor recovery. Previous research has shown that Constraint-Induced Movement Therapy (CIMT) might be effective in treating hemiparesis and supporting functional improvements in chronic patients, but that it might also be associated with higher costs in terms of demand, resources and inconvenience for the patient. Here, we offer a new therapeutic approach that combines CIMT with a positive reinforcement component. We suggest that this new therapy, called Reinforcement-Induced Movement Therapy (RIMT), might be similarly effective as CIMT and could be suitable for a broader population of chronic stroke patients. We first implemented a computational model to study the potential outcome of different CIMT and RIMT therapy combinations. Then we present the results of an ongoing clinical trial that supports predictions from the model. We conclude that an optimally combined CIMT and RIMT therapy might propose a novel and powerful rehabilitation approach, addressing the specific needs of chronic stroke patients.
  • Keywords
    "Computational modeling","Neurons","Medical treatment","Trajectory","Electronic mail","Predictive models","Planing"
  • Publisher
    ieee
  • Conference_Titel
    Virtual Rehabilitation Proceedings (ICVR), 2015 International Conference on
  • Electronic_ISBN
    2331-9569
  • Type

    conf

  • DOI
    10.1109/ICVR.2015.7358586
  • Filename
    7358586