Dysfunctional profile for patients in physical neurorehabilitation of upper limb

Villan Villan, M.A.; Pérez Rodríguez, Rodrigo; Gómez, C.; Opisso, Eloy; Tormos Muñoz, Josep M.; Medina, Josep y Gómez Aguilera, Enrique J. (2014). Dysfunctional profile for patients in physical neurorehabilitation of upper limb. En: "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.

Descripción

Título: Dysfunctional profile for patients in physical neurorehabilitation of upper limb
Autor/es:
  • Villan Villan, M.A.
  • Pérez Rodríguez, Rodrigo
  • Gómez, C.
  • Opisso, Eloy
  • Tormos Muñoz, Josep M.
  • Medina, Josep
  • Gómez Aguilera, Enrique J.
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: XIII Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON 2013)
Fechas del Evento: 25/09/2013 - 28/09/2013
Lugar del Evento: Sevilla, Spain
Título del Libro: IFMBE Proceedings
Fecha: 2014
Volumen: 41
Materias:
Palabras Clave Informales: Dysfunctional Profile, Classification, Physical Neurorehabilitation, Objective Assessment, Upper Limb
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Tecnología Fotónica [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 26114
Identificador DC: http://oa.upm.es/26114/
Identificador OAI: oai:oa.upm.es:26114
Identificador DOI: 10.1007/978-3-319-00846-2_438
Depositado por: Memoria Investigacion
Depositado el: 27 May 2014 18:02
Ultima Modificación: 01 Feb 2015 23:56
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