Automatically Grading Rey-Osterrieth Complex Figure Tests using Sketch Recognition

Anonymous

Keywords: Healthcare Information Systems, Gestural Input, Mobile Devices, Human-Computer Interaction, Sketch Recognition

TL;DR: We present a novel windows-based app designed to capture and automatically grade drawn rey-osterrieth complex figure tests, which are exams designed to test a subject's visuospatial ability, working memory, and planning.

Abstract: The Rey-Osterrieth Complex Figure Test (ROCF) is among the most widely used neuropsychological examinations to analyze visual spatial constructional ability and memory skills, but grading the patient’s sketched complex figure is subjective in nature and can be time consuming. With increasing demand for tools to help detect cognitive decline, there is a need to leverage sketch recognition research to assist in detecting fine details within an ROCF’s inherently abstract figure. We present a series of recognition algorithms to detect all 18 official ROCF details using a top-down sub-shape recognition approach. This automated grader transforms a sketch into an undirected graph, identifies and isolates detail sub-shapes, and validates sub-shape neatness via a point-density matrix template matcher. Experimental results from hand-drawn ROCFs confirm that our approach can automatically grade ROCF Tests on the same 18-item sketch detail checklist used by neuropsychologists with marginal error margin.

Track: HCI/visualization

Revision: No