A learning model for the automated assessment of hand-drawn images for visuo-spatial neglect rehabilitation.

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.

A new method for the synthesis of signature data with natural variability.

The collection of human biometric test data for system development and evaluation within any chosen modality generally requires significant time and effort if data are to be obtained in workable quantities. To overcome this problem, techniques to generate synthetic data have been developed. This paper describes a novel technique for the automatic synthesis of human handwritten-signature images, which introduces modeled variability within the generated output based on positional variation that is naturally found within genuine source data. The synthesized data were found to generate similar verification rates to those obtained using genuine data with the use of a commercial verification engine, thereby indicating the suitability of the data synthesized by using this method for a wide range of application scenarios.