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The block design test is a standardized, widely used neuropsychologicalassessment of visuospatial reasoning that involves a person recreating a seriesof given designs out of a set of colored blocks. In current testing procedures,an expert neuropsychologist observes a person's accuracy and completion time aswell as overall impressions of the person's problem-solving procedures, errors,etc., thus obtaining a holistic though subjective and often qualitative view ofthe person's cognitive processes. We propose a new framework that combines roomsensors and AI techniques to augment the information available toneuropsychologists from block design and similar tabletop assessments. Inparticular, a ceiling-mounted camera captures an overhead view of the tablesurface. From this video, we demonstrate how automated classification usingmachine learning can produce a frame-level description of the state of theblock task and the person's actions over the course of each test problem. Wealso show how a sequence-comparison algorithm can classify one individual'sproblem-solving strategy relative to a database of simulated strategies, andhow these quantitative results can be visualized for use by neuropsychologists.

Quantifying Human Behavior on the Block Design Test Through Automated Multi-Level Analysis of Overhead Video