The University of Southampton
University of Southampton Institutional Repository

A computer-based quantitative assessment of visuo-spatial neglect using regression and data transformation

A computer-based quantitative assessment of visuo-spatial neglect using regression and data transformation
A computer-based quantitative assessment of visuo-spatial neglect using regression and data transformation
Computer-based assessment systems analysing the drawing responses from a test subject have been widely explored within the area of neuropsychological dysfunction diagnosis and rehabilitation monitoring. This study reports on the development of a quantitative marking system for the automated assessment of visuo-spatial neglect. Using a clinically established pencil-and-paper based method as a marking benchmark, a set of features are extracted and selected from a battery of computer-captured drawing tasks. Through the application of linear regression and data transformation, the novel systemis shown to be effective in correlating against a clinically recognised scale,while simultaneously improving the efficiency of the testing process.
Computer-based hand-drawing analysis, Linear regression, Data transformation, Visuo-spatial neglect, Medical diagnosis
409-422
Liang, Yiqing
e6019ef2-d232-4bce-a224-fa21984a61d8
Guest, Richard M.
93533dbd-b101-491b-83cc-39ccfdc18165
Fairhurst, Michael C.
6a82d154-93fe-4657-bcee-934d5c888192
Potter, Jonathan M.
9f0adcdb-fe43-4c3b-b087-cd0d7ca687fc
Liang, Yiqing
e6019ef2-d232-4bce-a224-fa21984a61d8
Guest, Richard M.
93533dbd-b101-491b-83cc-39ccfdc18165
Fairhurst, Michael C.
6a82d154-93fe-4657-bcee-934d5c888192
Potter, Jonathan M.
9f0adcdb-fe43-4c3b-b087-cd0d7ca687fc

Liang, Yiqing, Guest, Richard M., Fairhurst, Michael C. and Potter, Jonathan M. (2009) A computer-based quantitative assessment of visuo-spatial neglect using regression and data transformation. Pattern Analysis Applications, 13 (4), 409-422. (doi:10.1007/s10044-009-0172-z).

Record type: Article

Abstract

Computer-based assessment systems analysing the drawing responses from a test subject have been widely explored within the area of neuropsychological dysfunction diagnosis and rehabilitation monitoring. This study reports on the development of a quantitative marking system for the automated assessment of visuo-spatial neglect. Using a clinically established pencil-and-paper based method as a marking benchmark, a set of features are extracted and selected from a battery of computer-captured drawing tasks. Through the application of linear regression and data transformation, the novel systemis shown to be effective in correlating against a clinically recognised scale,while simultaneously improving the efficiency of the testing process.

This record has no associated files available for download.

More information

Accepted/In Press date: 11 August 2009
Published date: 20 November 2009
Keywords: Computer-based hand-drawing analysis, Linear regression, Data transformation, Visuo-spatial neglect, Medical diagnosis

Identifiers

Local EPrints ID: 489661
URI: http://eprints.soton.ac.uk/id/eprint/489661
PURE UUID: d5f0e1fe-e7a1-4208-b9d5-35cabd24a9e1
ORCID for Richard M. Guest: ORCID iD orcid.org/0000-0001-7535-7336

Catalogue record

Date deposited: 30 Apr 2024 16:43
Last modified: 01 May 2024 02:10

Export record

Altmetrics

Contributors

Author: Yiqing Liang
Author: Richard M. Guest ORCID iD
Author: Michael C. Fairhurst
Author: Jonathan M. Potter

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×