Dysfunctional profile for patients in physical neurorehabilitation of upper limb

Villan Villan, M.A. and Pérez Rodríguez, Rodrigo and Gómez, C. and Opisso, Eloy and Tormos Muñoz, Josep M. and Medina, Josep and Gómez Aguilera, Enrique J. (2014). Dysfunctional profile for patients in physical neurorehabilitation of upper limb. In: "XIII Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON 2013)", 25/09/2013 - 28/09/2013, Sevilla, Spain. pp. 1775-1778. https://doi.org/10.1007/978-3-319-00846-2_438.

Description

Title: Dysfunctional profile for patients in physical neurorehabilitation of upper limb
Author/s:
  • Villan Villan, M.A.
  • Pérez Rodríguez, Rodrigo
  • Gómez, C.
  • Opisso, Eloy
  • Tormos Muñoz, Josep M.
  • Medina, Josep
  • Gómez Aguilera, Enrique J.
Item Type: Presentation at Congress or Conference (Article)
Event Title: XIII Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON 2013)
Event Dates: 25/09/2013 - 28/09/2013
Event Location: Sevilla, Spain
Title of Book: IFMBE Proceedings
Date: 2014
Volume: 41
Subjects:
Freetext Keywords: Dysfunctional Profile, Classification, Physical Neurorehabilitation, Objective Assessment, Upper Limb
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Tecnología Fotónica [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

This paper proposes a first approach to Objective Motor Assessment (OMA) methodology. Also, it introduces the Dysfunctional profile (DP) concept. DP consists of a data matrix characterizing the Upper Limb (UL) physical alterations of a patient with Acquired Brain Injury (ABI) during the rehabilitation process. This research is based on the comparison methology of UL movement between subjects with ABI and healthy subjects as part of OMA. The purpose of this comparison is to classify subjects according to their motor control and subsequently issue a functional assessment of the movement. For this purpose Artificial Neural Networks (ANN) have been used to classify patients. Different network structures are tested. The obtained classification accuracy was 95.65%. This result allows the use of ANNs as a viable option for dysfunctional assessment. This work can be considered a pilot study for further research to corroborate these results.

More information

Item ID: 26114
DC Identifier: http://oa.upm.es/26114/
OAI Identifier: oai:oa.upm.es:26114
DOI: 10.1007/978-3-319-00846-2_438
Deposited by: Memoria Investigacion
Deposited on: 27 May 2014 18:02
Last Modified: 01 Feb 2015 23:56
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