Design of a System for Early Detection and Treatment of Depression in Elderly Case Study

Edwing Almeida; Marco Ferruzca; María Pilar Morales Tlapanco

Pervasive Computing Paradigms for Mental Health. 4th International Symposium, MindCare 2014, Tokyo, Japan, May 8-9, 2014, Revised Selected Papers

Research Article

Design of a System for Early Detection and Treatment of Depression in Elderly Case Study

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@INPROCEEDINGS{10.1007/978-3-319-11564-1_12,
    author={Edwing Almeida and Marco Ferruzca and Mar\^{\i}a Pilar Morales Tlapanco},
    title={Design of a System for Early Detection and Treatment of Depression in Elderly Case Study},
    proceedings={Pervasive Computing Paradigms for Mental Health. 4th International Symposium, MindCare 2014, Tokyo, Japan, May 8-9, 2014, Revised Selected Papers},
    proceedings_a={MINDCARE},
    year={2014},
    month={12},
    keywords={Component Internet of things Elderly Depression Detection Pattern recognition},
    doi={10.1007/978-3-319-11564-1_12}
}

Edwing Almeida
Marco Ferruzca
María Pilar Morales Tlapanco
Year: 2014
Design of a System for Early Detection and Treatment of Depression in Elderly Case Study
MINDCARE
Springer
DOI: 10.1007/978-3-319-11564-1_12

Edwing Almeida¹^,*, Marco Ferruzca¹^,*, María Pilar Morales Tlapanco²^,*

1: Universidad Autónoma Metropolitana
2: Instituto Nacional de Rehabilitación

*Contact email: eaac@correo.azc.uam.mx, mvfn@correo.azc.uam.mx, mmorales@inr.gob.mx

Abstract

One of the major diseases that afflict the elderly population in Mexico is depression. This document describes the process of designing a system for early detection and treatment of the state of depression in older adults, taking advantage of the technological development of the Internet of Things, the Context Awareness and the concept of e-Health to determine the Daily Activities living (ADL) using the gesture recognition log events to determine an abnormality in as a means to conclude the variations in the ADL.

Keywords: Component Internet of things Elderly Depression Detection Pattern recognition

Published: 2014-12-10

: http://dx.doi.org/10.1007/978-3-319-11564-1_12