Next Article in Journal
Factors Influencing the Intention to Use the Common Ticketing System (Spider Card) in Thailand
Previous Article in Journal
Development of Emotional Intelligence through Physical Activity and Sport Practice. A Systematic Review
Open AccessArticle

Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity

1
Department of Biomedical Engineering, University of Connecticut, Storrs CT 06269, USA
2
Naval Submarine Medical Research Laboratory, Groton CT 06340, USA
*
Author to whom correspondence should be addressed.
Behav. Sci. 2019, 9(4), 45; https://doi.org/10.3390/bs9040045
Received: 1 April 2019 / Revised: 19 April 2019 / Accepted: 23 April 2019 / Published: 25 April 2019
Indices of heart rate variability (HRV) and electrodermal activity (EDA), in conjunction with machine learning models, were used to identify one of three tasks a subject is performing based on autonomic response elicited by the specific task. Using non-invasive measures to identify the task performed by a subject can help to provide individual monitoring and guidance to avoid the consequences of reduced performance due to fatigue or other stressors. In the present study, sixteen subjects were enrolled to undergo three tasks: The psychomotor vigilance task (PVT), an auditory working memory task (the n-back paradigm), and a visual search (ship search, SS). Electrocardiogram (ECG) (for HRV analysis) and EDA data were collected during the tests. For task-classification, we tested four machine learning classification tools: k-nearest neighbor classifier (KNN), support vector machines (SVM), decision trees, and discriminant analysis (DA). Leave-one-subject-out cross-validation was used to evaluate the performance of the constructed models to prevent overfitting. The most accurate models were the KNN (66%), linear SVM (62%), and linear DA (62%). The results of this study showed that it is possible to identify the task a subject is performing based on the subject’s autonomic reactions (from HRV and EDA). This information can be used to monitor individuals within a larger group to assist in reducing errors caused by uncoordinated or poor performance by allowing for automated tracking of and communication between individuals. View Full-Text
Keywords: heart rate variability; electrodermal activity; autonomic nervous system; psychomotor vigilance task; working memory; ship search heart rate variability; electrodermal activity; autonomic nervous system; psychomotor vigilance task; working memory; ship search
Show Figures

Figure 1

MDPI and ACS Style

Posada-Quintero, H.F.; Bolkhovsky, J.B. Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity. Behav. Sci. 2019, 9, 45. https://doi.org/10.3390/bs9040045

AMA Style

Posada-Quintero HF, Bolkhovsky JB. Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity. Behavioral Sciences. 2019; 9(4):45. https://doi.org/10.3390/bs9040045

Chicago/Turabian Style

Posada-Quintero, Hugo F.; Bolkhovsky, Jeffrey B. 2019. "Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity" Behav. Sci. 9, no. 4: 45. https://doi.org/10.3390/bs9040045

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop