Classification of Alzheimer's patients through ubiquitous computing

Abstract: Functional data analysis and artificial neural networks are the building blocks of the proposed methodology that distinguishes the movement patterns among c?s patients on different stages of the disease and classifies new patients to their appropriate stage of the disease. The movement patterns are obtained by the accelerometer device of android smartphones that the patients carry while moving freely. The proposed methodology is relevant in that it is flexible on the type of data to which it is applied. To exemplify that, it is analyzed a novel real three-dimensional functional dataset where each datum is observed in a different time domain. Not only is it observed on a difference frequency but also the domain of each datum has different length. The obtained classification success rate of 83% indicates the potential of the proposed methodology

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Fuente: Sensors 2017, 17, 1679

Editorial: MDPI

 Fecha de publicación: 21/07/2017

Nº de páginas: 18

Tipo de publicación: Artículo de Revista

DOI: 10.3390/s17071679

ISSN: 1424-8220

Proyecto español: MTM2014-55262-P ; MTM2014-56235-C2-2-P