@article{10902/13296,
year = {2017},
month = {7},
url = {http://hdl.handle.net/10902/13296},
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},
organization = {This work was partially supported by project PAC::LFO of the Spanish Programa Estatal de
Fomento de la Investigación Científica y Técnica de Excelencia under grant MTM2014-55262-P, and by the Spanish Ministerio de Economía y Competitividad under grant MTM2014-56235-C2-2-P. We gratefully acknowledge the “Asociación de Familiares de Enfermos de Alzheimer en Cantabria” and Pablo Cobo García for their participation in the various studies.},
publisher = {MDPI},
publisher = {Sensors 2017, 17, 1679},
title = {Classification of Alzheimer's patients through ubiquitous computing},
author = {Nieto Reyes, Alicia and Duque Medina, Rafael and Montaña Arnaiz, José Luis and Lage, Carmen},
}