Iranian Journal of Electrical and Electronic Engineering
Iran University of Science and Technology
Tue, Aug 13, 2024
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Volume 20, Issue 1 (March 2024)
IJEEE 2024, 20(1): 12-20 |
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Karimiyan Abdar A, Esteki A, Sheikh Hassani M. Quantitative Analysis of Dual Task Cost Based on Different Cognitive Difficulties. IJEEE 2024; 20 (1) :12-20
URL: http://ijeee.iust.ac.ir/article-1-2858-en.html
URL: http://ijeee.iust.ac.ir/article-1-2858-en.html
Abstract: (416 Views)
The impact of cognitive tasks on human movement is of practical significance; we hereby aim to demonstrate that a significant relationship exists between the dual task’s cognitive demand and the disruption caused in hand movement, with the hope to extend this experiment to subjects with disorders (MS, CP, stroke patients) in future studies. By doing so, we hope to be able to develop a metric for evaluating their disease levels using our method and minimize clinical interventions. While previous research has predominantly focused on lower body activities, this study explores the effect of dual tasks on hand movements in healthy individuals.
A simulated finger-to-nose test combined with a standard reverse counting task, featuring four difficulty levels, was conducted. Utilizing SVM and decision tree classifiers, we analyzed various features to discern the impact of cognitive tasks on hand movements, including completed cycles and idle time at markers. Our findings reveal that features such as entropy and kurtosis effectively distinguish between task difficulty levels and hand movement disruption. The classifiers achieved accuracies of 70% and 74% for decision tree and SVM, respectively. We hope extending this research to diseased subjects could potentially provide a more accurate assessment of disease severity through the measurement of hand movements during cognitive tasks, offering a non-clinical alternative for disease evaluation.
A simulated finger-to-nose test combined with a standard reverse counting task, featuring four difficulty levels, was conducted. Utilizing SVM and decision tree classifiers, we analyzed various features to discern the impact of cognitive tasks on hand movements, including completed cycles and idle time at markers. Our findings reveal that features such as entropy and kurtosis effectively distinguish between task difficulty levels and hand movement disruption. The classifiers achieved accuracies of 70% and 74% for decision tree and SVM, respectively. We hope extending this research to diseased subjects could potentially provide a more accurate assessment of disease severity through the measurement of hand movements during cognitive tasks, offering a non-clinical alternative for disease evaluation.
Type of Study: Research Paper |
Subject:
Biomedical Signal Processing
Received: 2023/04/04 | Revised: 2024/05/13 | Accepted: 2024/03/24
Received: 2023/04/04 | Revised: 2024/05/13 | Accepted: 2024/03/24
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