Quantifying Human Behavior on the Block Design Test Through Automated Multi-Level Analysis of Overhead Video
This work addresses the need for objective, quantitative neuropsychological assessments for clinicians, though it is incremental as it builds on existing sensor and AI techniques.
The authors tackled the problem of subjective and qualitative assessment in the block design test by developing an automated framework using overhead video and AI to quantify block states and actions, achieving frame-level classification and strategy comparison.
The block design test is a standardized, widely used neuropsychological assessment of visuospatial reasoning that involves a person recreating a series of given designs out of a set of colored blocks. In current testing procedures, an expert neuropsychologist observes a person's accuracy and completion time as well as overall impressions of the person's problem-solving procedures, errors, etc., thus obtaining a holistic though subjective and often qualitative view of the person's cognitive processes. We propose a new framework that combines room sensors and AI techniques to augment the information available to neuropsychologists from block design and similar tabletop assessments. In particular, a ceiling-mounted camera captures an overhead view of the table surface. From this video, we demonstrate how automated classification using machine learning can produce a frame-level description of the state of the block task and the person's actions over the course of each test problem. We also show how a sequence-comparison algorithm can classify one individual's problem-solving strategy relative to a database of simulated strategies, and how these quantitative results can be visualized for use by neuropsychologists.