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Autoadaptive neurorehabilitation robotic system assessment with a post-stroke patient
Morales Vidal, Ricardo; Badesa Clemente, Francisco Javier; García Aracil, Nicolás; Aranda López, Juan; Casals Gelpi, Alicia
Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Universitat Politècnica de Catalunya. GRINS - Robòtica Intel·ligent i Sistemes
This paper presents a new rehabilitation system that is able to adapt its performance to patient's psychophysiological state during the execution of robotic rehabilitation tasks. Using this approach, the motivation and participation of the patient during rehabilitation activity can be maximized. In this paper, the results of the study with healthy subjects presented in (Badesa et al., 2014b) have been extended for using them with patients who have suffered a stroke. In the first part of the article, the different components of the adaptive system are exposed, as well as a comparison of different machine learning techniques to classify the patient's psychophysiological state between three possible states: stressed, average excitation level and relaxed are presented. Finally, the results of the auto-adaptive system which modifies the behavior of the rehabilitation robot and virtual task in function of measured physiological signals are shown for a patient in the chronic phase of stroke.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Robòtica
Robotics in medicine
Cerebrovascular disease -- Patients -- Rehabilitation
Physiological state
Multimodal interfaces
Rehabilitation robotics
Estado fisiológico
Interfaces multimodales
Robótica de rehabilitación
Robòtica en medicina
Malalties cerebrovasculars -- Pacients -- Rehabilitació

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