Classification of Stabilometric Time-Series Using an Adaptive Fuzzy Inference Neural Network System

Lara Torralbo, Juan Alfonso and Jahankhani, Pari and Pérez Pérez, Aurora and Caraça-Valente Hernández, Juan Pedro and Kodogiannis, Vassilis (2010). Classification of Stabilometric Time-Series Using an Adaptive Fuzzy Inference Neural Network System. In: "10th International Conference, ICAISC 2010", 13/06/2010 - 17/06/2010, Zakopane, Polonia. ISBN 978-3-642-13207-0.

Description

Title: Classification of Stabilometric Time-Series Using an Adaptive Fuzzy Inference Neural Network System
Author/s:
  • Lara Torralbo, Juan Alfonso
  • Jahankhani, Pari
  • Pérez Pérez, Aurora
  • Caraça-Valente Hernández, Juan Pedro
  • Kodogiannis, Vassilis
Item Type: Presentation at Congress or Conference (Article)
Event Title: 10th International Conference, ICAISC 2010
Event Dates: 13/06/2010 - 17/06/2010
Event Location: Zakopane, Polonia
Title of Book: Proceedings of the 10th International Conference, ICAISC 2010
Date: June 2010
ISBN: 978-3-642-13207-0
Volume: 6113
Subjects:
Faculty: Facultad de Informática (UPM)
Department: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Stabilometry is a branch of medicine that studies balance-related human functions. The analysis of stabilometric-generated time series can be very useful to the diagnosis and treatment balance-related dysfunctions such as dizziness. In stabilometry, the key nuggets of information in a time series signal are concentrated within definite time periods known as events. In this study, a feature extraction scheme has been developed to identify and characterise the events. The proposed scheme utilises a statistical method that goes through the whole time series from the start to the end, looking for the conditions that define events, according to the experts¿ criteria. Based on these extracted features, an Adaptive Fuzzy Inference Neural Network (AFINN) has been applied for the classification of stabilometric signals. The experimental results validated the proposed methodology.

More information

Item ID: 7562
DC Identifier: http://oa.upm.es/7562/
OAI Identifier: oai:oa.upm.es:7562
Official URL: http://www.springerlink.com/content/u191762207003q56/
Deposited by: Memoria Investigacion
Deposited on: 21 Jun 2011 11:45
Last Modified: 20 Apr 2016 16:40
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