A Learning Model for the Automated Assessment of Hand-Drawn Images for Visuo-Spatial Neglect Rehabilitation

Liang, Y. and Fairhurst, M.C. and Guest, R.M. and Potter, J.M. (2010) A Learning Model for the Automated Assessment of Hand-Drawn Images for Visuo-Spatial Neglect Rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18 (5). pp. 560-570. ISSN 1534-4320. (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1109/TNSRE.2010.2047605

Abstract

Visuo-spatial neglect (often simply referred to as “neglect”) is a complex poststroke medical syndrome which may be assessed by means of a series of drawing-based tests. Based on a novel analysis of a test battery formed from established pencil-and-paper tests, the aim of this study is to develop an automated assessment system which enables objectivity, repeatability, and diagnostic capability in the scoring process. Furthermore, the novel assessment system encapsulates temporal sequence and other “dynamic” information inherent in the drawing process. Several approaches are introduced in this paper and the results compared. The optimal model is shown to produce significant agreement with the score for drawing-related components of the Rivermead Behavioural Inattention Test, the widely accepted standardised clinical test for the diagnosis of neglect, and, more importantly, to encapsulate data to enable an enhanced test resolution with a reduction in battery size.

Item Type: Article
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics (see also: telecommunications) > TK7880 Applications of electronics (inc industrial & domestic) > TK7882.P3 Pattern Recognition
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: J. Harries
Date Deposited: 25 Mar 2011 15:24
Last Modified: 04 Oct 2012 12:58
Resource URI: http://kar.kent.ac.uk/id/eprint/27595 (The current URI for this page, for reference purposes)
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