Volume 18, Issue 4 (Journal of Control, V.18, N.4 Winter 2025)
JoC 2025, 18(4): 95-105 |
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Etaati S, Delrobaei M. Assessment of Individuals' Attention Levels Using Machine Learning and Analysis of Eye and Hand Movement Patterns in the Trail-Making Test. JoC 2025; 18 (4) :95-105
URL: http://joc.kntu.ac.ir/article-1-1056-en.html
URL: http://joc.kntu.ac.ir/article-1-1056-en.html
1- K. N. Toosi University of Technology
Abstract: (506 Views)
Attention, as one of the key cognitive processes, plays a central role in daily activities, learning, and human-environment interactions. Accurately and objectively assessing individuals' attention levels, particularly in dynamic and real-world situations, has always been a challenge. Traditional methods, such as self-report questionnaires or paper-based tests, often fail to capture momentary attention fluctuations or the impact of environmental factors. This study, aiming to provide a precise and efficient method, utilized the analysis of eye and hand movement patterns within the framework of the Trail-Making Test. Data from 42 healthy participants were collected while they performed the test. Their eye and hand movements were accurately measured using eye-tracking technology and mouse movement tracking. Features such as saccades, fixations, blinks, and mouse movement speed were extracted. A Random Forest model was then trained using these features to predict attention levels. The results indicate that the model achieved a coefficient of determination R² score of 72%, demonstrating its ability to predict attention levels accurately. These findings confirm that eye and hand movement patterns can serve as reliable indicators for attention assessment. Therefore, applying machine learning techniques to analyze eye and hand movement data presents a reliable approach for evaluating attention levels in real-world settings. Beyond its scientific and research significance, this approach has practical applications in various fields, including education, clinical psychology, and the design of human-computer interaction systems.
Keywords: Cognitive assessment, executive function, trail-making test (TMT), human-robot interaction, machine learning
Type of Article: Research paper |
Subject:
Special
Received: 2024/11/27 | Accepted: 2025/03/10 | ePublished ahead of print: 2025/03/10 | Published: 2025/03/18
Received: 2024/11/27 | Accepted: 2025/03/10 | ePublished ahead of print: 2025/03/10 | Published: 2025/03/18
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