ArticleBehavioral sciences (Basel, Switzerland)2019
Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity.
Article in Behavioral sciences (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Wearable Biosensor Technology in Education: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Frontal Alpha Asymmetry and Electrodermal Activity: A Mutual Information Analysis Across Cognitive Load and Sleep Deprivation.Biosensors · 2026Article
- Distinguishing between the exponential and Lindley distributions: An illustration from biological psychology.Behavior research methods · 2026Article
- Understanding the shortcomings of heart rate variability as a tool for autonomic analysis.Frontiers in physiology · 2026Article
- Are you afraid? Neurophysiological responses to multidimensional fear in an interactive semi-immersive environment.Frontiers in systems neuroscience · 2026Article
- Exploring cognitive load through neuropsychological features: an analysis using fNIRS-eye tracking.Medical & biological engineering & computing · 2025Article
- Wearable neurofeedback acceptance model for students' stress and anxiety management in academic settings.PloS one · 2024Article
- Sex differences in autonomic functions and cognitive performance during cold-air exposure and cold-water partial immersion.Frontiers in physiology · 2024Article
- Mutual Information between EDA and EEG in Multiple Cognitive Tasks and Sleep Deprivation Conditions.Behavioral sciences (Basel, Switzerland) · 2023Article
- Wearable Multisensor Ring-Shaped Probe for Assessing Stress and Blood Oxygenation: Design and Preliminary Measurements.Biosensors · 2023Article
- The Concept of Advanced Multi-Sensor Monitoring of Human Stress.Sensors (Basel, Switzerland) · 2021Review
- Color-dependent changes in humans during a verbal fluency task under colored light exposure assessed by SPA-fNIRS.Scientific reports · 2021Article
- Emotion Recognition Using Electrodermal Activity Signals and Multiscale Deep Convolutional Neural Network.Journal of medical systems · 2021Article
- Trends in Heart-Rate Variability Signal Analysis.Frontiers in digital health · 2021Review
- Machine Learning Models for the Classification of Sleep Deprivation Induced Performance Impairment During a Psychomotor Vigilance Task Using Indices of Eye and Face Tracking.Frontiers in artificial intelligence · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.