Journal
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Volume 18, Issue 7, Pages 1253-1266Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218001404003757
Keywords
computer-based drawing analysis; visuo-spatial neglect; feature selection; sequence analysis
Categories
Ask authors/readers for more resources
The reported work aims to objectively and accurately assess the post-stroke clinical condition of visuo-spatial neglect using a series of standardized geometric shape drawing tasks. We present a method implementing existing pencil-and-paper diagnostic methods and define a set of static and dynamic features that can be extracted from drawing responses captured online using a graphics tablet. We also present a method for automatically assessing the constructional sequence of the drawing using Hidden Markov Models. The method enables the automated extraction, position identification and drawing order of individual sides of a shape within a drawing. Discrimination between two populations (a neglect population and stroke subjects without neglect as determined by existing standard assessment methods) using a combination of performance features and constructional sequence is examined across three separate drawing tasks. Results from experimentation show how a combination of sequence and performance features is able to generalize across a wide variety of input samples and obtain a diagnostic classification which can be used alongside other forms of conventional assessment. Furthermore, the application of a multi-classifier combination strategy leads to a significant increase in recognition ability.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available