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Quantifying Human Behavior on the Block Design Test Through Automated Multi-Level Analysis of Overhead Video
Computer Vision and Pattern Recognition · Submitted 19 Nov 2018
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | 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. | background 0.75 | 0.05 | 0.02 | asserted 1.00 |
| s2 | 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. | contribution 1.00 | 0.55 | 0.23 | asserted 0.86 |
| s3 | In particular, a ceiling-mounted camera captures an overhead view of the table surface. | method 0.99 | 0.75 | 0.02 | asserted 0.99 |
| s4 | 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. | method 0.50 | 0.95 | 0.17 | asserted 1.00 |
| s5 | 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. | contribution 0.67 | 0.95 | 0.33 | asserted 1.00 |
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The paper
Original abstract
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.
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