A deep learning model for assistive decision-making during robot-aided rehabilitation therapies based on therapists' demonstrations.

Journal: Journal of neuroengineering and rehabilitation
PMID: 39891159

Abstract

BACKGROUND: A promising approach to improving motor recovery during rehabilitation is the use of robotic rehabilitation devices. These robotic devices provide tools to monitor the patient's recovery progress while providing highly standardized and intensive therapy. A major challenge in using these robotic devices is the ability to decide when to assist the user. In this context, we propose a Deep Learning-based solution that can learn from a therapist's criteria when a patient needs assistance during robot-aided rehabilitation therapy.

Authors

  • David Martínez-Pascual
    Robotics and Artificial Intelligence Group of the Bioengineering Institute, Miguel Hernández University, Avda. de la Universidad, 03202, Elche, Spain.
  • Jose M Catalan
    Neuro-Bioengineering Research Group, Miguel Hernandez University, Avda. de la Universidad W/N, 03202 Elche, Spain. jose.catalan@goumh.umh.es.
  • Luis D Lledó
    Biomedical Neuroengineering Group, Universidad Miguel Hernández, Elche, Alicante, Spain.
  • Andrea Blanco-Ivorra
    Robotics and Artificial Intelligence Group of the Bioengineering Institute, Miguel Hernández University, Avda. de la Universidad, 03202, Elche, Spain.
  • Nicolás Garcia-Aracil
    Biomedical Neuroengineering Group, Universidad Miguel Hernández, Elche, Alicante, Spain.

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