A Fuzzy Recommendation System for the Automatic Personalization of Physical Rehabilitation Exercises in Stroke Patients

Cristian Gmez-Portes, José Jesús Castro-Schez, Javier Albusac, Dorothy N. Monekosso and David Vallejo
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Cristian Gmez-Portes: Department of Information Technologies and Systems, University of Castilla-La Mancha Paseo de la Universidad 4, 13071 Ciudad Real, Spain
José Jesús Castro-Schez: Department of Information Technologies and Systems, University of Castilla-La Mancha Paseo de la Universidad 4, 13071 Ciudad Real, Spain
Javier Albusac: Department of Information Technologies and Systems, University of Castilla-La Mancha Paseo de la Universidad 4, 13071 Ciudad Real, Spain
Dorothy N. Monekosso: School of Built Environment, Engineering and Computing, Leeds-Beckett University, Leeds LS6 3QT, UK
David Vallejo: Department of Information Technologies and Systems, University of Castilla-La Mancha Paseo de la Universidad 4, 13071 Ciudad Real, Spain

Mathematics, 2021, vol. 9, issue 12, 1-24

Abstract: Stroke is among the top 10 leading causes of death and disability around the world. Patients who suffer from this disease usually perform physical exercises at home to improve their condition. These exercises are recommended by therapists based on the patient’s progress level, and may be remotely supervised by them if technology is an option for both. At this point, two major challenges must be faced. The first one is the lack of specialized medical staff to remotely handle the growing number of stroke patients. The second one is the difficulty of dynamically adapt the patient’s therapy plan in real time whilst they rehabilitate at home, since their evolution varies as the rehabilitation process progresses. In this context, we present a fuzzy system that is able to automatically adapt the rehabilitation plan of stroke patients. The use of fuzzy logic greatly facilitates the monitoring and guidance of stroke patients. Moreover, the system is capable of automatically generating modifications of existent exercises whilst considering their particularities at any given time. A preliminary experiment was conducted to show the advantages of the proposal, and the results suggest that the application of fuzzy logic may help make correct decisions based on the patient’s progress level.

Keywords: remote rehabilitation; recommender system; stroke; fuzzy logic; telemedicine (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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