@conference{10902/18337,
year = {2019},
url = {http://hdl.handle.net/10902/18337},
abstract = {Alzheimer’s disease (AD) constitutes a neurodegenerative pathology that presents mobility disorders as one of its earliest symptoms. Current smartphones integrate accelerometers that can be used to collect mobility data of Alzheimer’s patients. This paper describes a method that processes these accelerometer data and a convolutional neural network (CNN) that classifies the stage of the disease according to the mobility patterns of the patient. The method is applied in a case study with 35 Alzheimer’s patients, in which a classification success rate of 91% was obtained},
publisher = {MDPI},
publisher = {Proceedings 2019, 31(1), 72},
title = {A Convolutional Neural Network-Based Method for Human Movement Patterns Classification in Alzheimer?s Disease},
author = {Salomón García, Sergio and Duque Medina, Rafael and Bringas Tejero, Santos and Montaña Arnaiz, José Luis and Lage, Carmen},
}