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Review of Automated Systems for Upper Limbs Functional Assessment in Neurorehabilitation

Authors :
Carlos Balaguer
Alberto Jardón Huete
Patricia Sánchez-Herrera Baeza
Edwin Daniel Ona Simbana
Comunidad de Madrid
Ministerio de Economía y Competitividad (España)
Source :
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid, instname, IEEE Access, Vol 7, Pp 32352-32367 (2019)
Publication Year :
2019
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2019.

Abstract

Traditionally, the assessment of upper limb (UL) motor function in neurorehabilitation is carried out by clinicians using standard clinical tests for objective evaluation, but which could be influenced by the clinician's subjectivity or expertise. The automation of such traditional outcome measures (tests) is an interesting and emerging field in neurorehabilitation. In this paper, a systematic review of systems focused on automation of traditional tests for assessment of UL motor function used in neurological rehabilitation is presented. A systematic search and review of related articles in the literature were conducted. The chosen works were analyzed according to the automation level, the data acquisition systems, the outcome generation method, and the focus of assessment. Finally, a series of technical requirements, guidelines, and challenges that must be considered when designing and implementing fully-automated systems for upper extremity functional assessment are summarized. This paper advocates the use of automated assessment systems (AAS) to build a rehabilitation framework that is more autonomous and objective. This work was supported in part by the Spanish Ministry of Economy and Competitiveness via the ROBOHEALTH (DPI2013-47944-C4-1-R) and ROBOESPAS (DPI2017-87562-C2-1-R) Projects, and in part by the RoboCity2030-III-CM project (S2013/MIT-2748) which is funded by the Programas de Actividades I+D Comunidad de Madrid and cofunded by the Structural Funds of the EU.

Details

ISSN :
21693536
Volume :
7
Database :
OpenAIRE
Journal :
IEEE Access
Accession number :
edsair.doi.dedup.....17a95ee7e48d956e1f392a8b5e919fba
Full Text :
https://doi.org/10.1109/access.2019.2901814