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

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.

Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education

“…Topics addressed in this Special Issue include data mining, machine learning, learning analytics, prediction methods, pattern recognition, decision analysis, probabilistic reasoning, fuzzy systems, student or patient modelling, adaptive systems, collaborative systems, recommendation systems, experimental design, and empirical study cases. Specifically, there are twenty rigorously reviewed papers included in this Special Issue, with eleven specializing in the field of medicine and health [1][2][3][4][5][6][7][8][9][10][11] and nine specializing in the field of education [12][13][14][15][16][17][18][19][20]. In total, fifteen papers (43% of the received) were rejected for publication.…”

“…In the area of health, most papers aim at improving the diagnosis of a disease, such as influenza [3], Alzheimer's disease [7], and cardiac arrhythmias [11], or of a specific symptom, such as changes in the retinal vasculature [1], mental fatigue [4], gait variability [5] or vitamin D deficiency [8]. There is one for the personalization of rehabilitation exercises for stroke patients [6], and there are three theoretical papers [2,9,10] aimed at improving the representation of models, which can be applied to different and actual medical problems.…”

“…Regarding the techniques used, those related to artificial intelligence prevail. Specifically, the majority make use of different machine learning algorithms [1][2][3][4]6,8,11], although with distinct objectives. For example, some works propose new specific classification models.…”

“…A different proposal is that of [6] in which fuzzy logic was used to propose a system capable of recommending rehabilitation exercises for stroke patients depending on the variation in the patient's level of progress as the rehabilitation process evolves.…”

“…It should be noted that the data used for the above studies have been obtained from public repositories in papers [1,2,11]. The other papers obtain their data from different types of experimental studies, most of them conducted with individuals [3][4][5][6][7][8].…”