Clock drawing test digit recognition using static and dynamic features

Harbi, Zainab, Hicks, Yulia Alexandrovna ORCID: https://orcid.org/0000-0002-7179-4587 and Setchi, Rossitza M. ORCID: https://orcid.org/0000-0002-7207-6544 2016. Clock drawing test digit recognition using static and dynamic features. Procedia Computer Science 96 , pp. 1221-1230. 10.1016/j.procs.2016.08.166

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Abstract

The clock drawing test (CDT) is a standard neurological test for detection of cognitive impairment. A computerised version of the test promises to improve the accessibility of the test in addition to obtaining more detailed data about the subject's performance. Automatic handwriting recognition is one of the first stages in the analysis of the computerised test, which produces a set of recognized digits and symbols together with their positions on the clock face. Subsequently, these are used in the test scoring. This is a challenging problem because the average CDT taker has a high likelihood of cognitive impairment, and writing is one of the first functional activities to be affected. Current handwritten digit recognition system perform less well on this kind of data due to its unintelligibility. In this paper, a new system for numeral handwriting recognition in the CDT is proposed. The system is based on two complementary sources of data, namely static and dynamic features extracted from handwritten data. The main novelty of this paper is the new handwriting digit recognition system, which combines two classifiers—fuzzy k-nearest neighbour for dynamic stroke-based features and convolutional neural network for static image- based features, which can take advantage of both static and dynamic data. The proposed digit recognition system is tested on two sets of data: first, Pendigits online handwriting digits; and second, digits from the actual CDTs. The latter data set came from 65 drawings made by healthy people and 100 drawings reproduced from the drawings by dementia patients. The test on both data sets shows that the proposed combination system can outperform each classifier individually in terms of recognition accuracy, especially when assessing the handwriting of people with dementia

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords:	Clock Drawing Test (CDT); cognitive impairment diagnosis; dementia; static and dynamic representation
Additional Information:	PDF uploaded in accordance with publisher's policies at http://www.sherpa.ac.uk/romeo/issn/1877-0509/ (accessed 28.7.16).
Publisher:	Elsevier
ISSN:	1877-0509
Date of First Compliant Deposit:	22 July 2016
Date of Acceptance:	27 May 2016
Last Modified:	07 Jul 2023 21:35
URI:	https://orca.cardiff.ac.uk/id/eprint/93185

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