Abstract
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.
Highlights
Professions require individuals to perform tasks that demand different skills and present various physiological challenges, including fast reaction to visual or auditory commands, working memory, visual search, or staying awake for extended periods of time, among others
We performed three steps of analysis: First, we looked for changes in the indices of heart rate variability (HRV) and electrodermal activity (EDA) between the three tasks using a repeated-measurements analysis, to assess how sensitive those indices are to the differences in the tasks; second, we trained different models to classify the different tasks using the HRV and EDA indices, to determine the best model and the most useful indices for each task; and third, we evaluated the performance of the best models in the trials performed by the subjects during a 24-h period of wakefulness
Indices of HRV and EDA obtained during the baseline, psychomotor vigilance task (PVT), n-back, and for a given participant
Summary
Professions require individuals to perform tasks that demand different skills and present various physiological challenges, including fast reaction to visual or auditory commands, working memory, visual search, or staying awake for extended periods of time, among others. The stress produced by different tasks has different components, resulting in task-type specific autonomic responses [2,3]. Using the specific effects of a given task on the sympathetic and vagal (parasympathetic) branches of the autonomic nervous system (ANS) to identify the task being performed will help to deploy strategies to foster human performance and minimize the risk and economic burden that the misperforming of different tasks represents [6,7]. We measured ANS response to three different tasks: Psychomotor vigilance task, auditory working memory, and continuous visual search.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
