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A Convolutional Neural Network-Based Method for Human Movement Patterns Classification in Alzheimer?s Disease

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.

 Congreso: International Conference on Ubiquitous Computing and Ambient Intelligence: UCAmI (13th : 2019 : Toledo)

Editorial: MDPI

 Año de publicación: 2019

Nº de páginas: 9

Tipo de publicación: Comunicación a Congreso

ISSN: 2504-3900

Url de la publicación: https://doi.org/10.3390/proceedings2019031072

Autores/as

SALOMÓN, SERGIO

SANTOS BRINGAS TEJERO

LAGE, CARMEN