Personalized Recommendation System for Efficient Integrated Cognitive Rehabilitation Training Based on Bigdata

In this study, a personalized recommendation system for efficient integrated cognitive rehabilitation training based on bigdata was developed. The system consists of 5 main phases (collection, storage, processing, analyzing, visualization). First, in the pre-processing process before the collection phase, resulting scores from multiple cognitive rehabilitation contents and patients’ personal information are saved in database. In the collection/storage phases, the patient information saved in the database is saved in bigdata platform. In the processing phase, the data are processed/refined in a necessary form to be utilized in the analysis and statistical processing program, R. Lastly, in the analysis/visualization phases, personalized contents of integrated cognitive rehabilitation training are recommended to patients using the K-Means method of the unsupervised learning algorithms and spiral model through patients’ personal information, MMSE results, cognitive rehabilitation contents results based on the processed/refined data. Patients can utilize the personalized recommendation system for integrated cognitive rehabilitation training based on bigdata to implement cognitive function evaluation and personalized training at home.