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Stress Detection by means of Stress Physiological Template
Cita
Santos Sierra, Alberto de, Sánchez Ávila, María del Carmen 
ORCID: https://orcid.org/0000-0002-7690-1011, Bailador del Pozo, Gonzalo and Guerra Casanova, Javier
(2011).
Stress Detection by means of Stress Physiological Template.
En: "2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC)", 19/10/2011 - 21/10/2011, Salamanca, España. ISBN 978-1-4577-1122-0. pp. 131-136.
ORCID: https://orcid.org/0000-0002-7690-1011, Bailador del Pozo, Gonzalo and Guerra Casanova, Javier
(2011).
Stress Detection by means of Stress Physiological Template.
En: "2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC)", 19/10/2011 - 21/10/2011, Salamanca, España. ISBN 978-1-4577-1122-0. pp. 131-136.
Descripción
| Título: | Stress Detection by means of Stress Physiological Template |
|---|---|
| Autor/es: |
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| Tipo de Documento: | Ponencia en Congreso o Jornada (Artículo) |
| Título del Evento: | 2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC) |
| Fechas del Evento: | 19/10/2011 - 21/10/2011 |
| Lugar del Evento: | Salamanca, España |
| Título del Libro: | Proceedings of 2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC) |
| Fecha: | 2011 |
| ISBN: | 978-1-4577-1122-0 |
| Materias: | |
| ODS: | |
| Escuela: | Centro de Domótica Integral (CeDInt) (UPM) |
| Departamento: | Otro |
| Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
Resumen
This paper describes a stress detection system based on fuzzy logic and two physiological signals: Galvanic Skin Response and Heart Rate. Instead of providing a global stress classification, this approach creates an individual stress templates, gathering the behaviour of individuals under situations with different degrees of stress. The proposed method is able to detect stress properly with a rate of 99.5%, being evaluated with a database of 80 individuals. This result improves former approaches in the literature and well-known machine learning techniques like SVM, k-NN, GMM and Linear Discriminant Analysis. Finally, the proposed method is highly suitable for real-time applications
Más información
| ID de Registro: | 13515 |
|---|---|
| Identificador DC: | https://oa.upm.es/13515/ |
| Identificador OAI: | oai:oa.upm.es:13515 |
| URL Oficial: | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?... |
| Depositado por: | Memoria Investigacion |
| Depositado el: | 17 Oct 2012 11:16 |
| Ultima Modificación: | 04 Jul 2024 06:42 |
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