ArticleSensors (Basel, Switzerland)2023
A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 67 citations in OpenAlex.
- Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review.Biosensors · 2025Pooled it
- Driving-Context Classification from Wearable Physiological and Motion Signals During Real-World Driving.Sensors (Basel, Switzerland) · 2026Article
- Interventional Feasibility Study Investigating Virtual Reality-Based Gait Assessment With Orthoses in People With Multiple Sclerosis: A Study Protocol.Clinical and translational science · 2026Article
- Digital and Biomarker-Based Monitoring for Schizophrenia-Treatment Adherence.Neuropsychopharmacology reports · 2026Review
- RheumaPain: a multimodal biosignal dataset for objective pain assessment in pediatric rheumatic diseases.Data in brief · 2026Article
- Reliability Assessment of Wearable Technologies for Physiological Measurements: An Evaluation of Shimmer3 GSR+, Empatica E4, EmbracePlus, and Pixel Watch 2 Across Cognitive, Affective and Physical Activity Tasks.Sensors (Basel, Switzerland) · 2026Article
- Using Wearable Technology to Predict the Occurrence of Severe Behavior Problems among Neurodiverse Individuals: A Systematic Review.Perspectives on behavior science · 2026Review
- Convergent Sensing: Integrating Biometric and Environmental Monitoring in Next-Generation Wearables.Biosensors · 2026Review
- Article
- Stress and Emotion Open Access Data: A Review on Datasets, Modalities, Methods, Challenges, and Future Research Perspectives.Journal of healthcare informatics research · 2025Review
- Exploring the Impact of Home-Based Serious Smartphone Resuscitation Gaming on Stress Among Nursing Students Practicing Simulated Adult Basic Life Support: Randomized Waitlist Controlled Trial.JMIR serious games · 2025Article
- A Conceptual Framework for User Trust in AI Biosensors: Integrating Cognition, Context, and Contrast.Sensors (Basel, Switzerland) · 2025Review
- Human Contributions to Safety Data Testbed Flight Simulation Study: Data Methods, Processing, and Quality.Scientific data · 2025Article
- Longitudinal observation of psychophysiological data as a novel approach to personalised postural defect rehabilitation.Scientific reports · 2025Article
- EDA, PPG and Skin Temperature as Predictive Signals for Mental Failure by a Statistical Analysis on Stress and Mental Workload.IEEE open journal of engineering in medicine and biology · 2025Article
- Overcoming behavioral variability in electromyography signals by an adaptive incremental classification approach.Computational and structural biotechnology journal · 2025Article
- Machine Learning and Wearable Technology: Monitoring Changes in Biomedical Signal Patterns during Pre-Migraine Nights.Healthcare (Basel, Switzerland) · 2024Article
- Electrodermal Activity (EDA) Morphologies and Prediction of Engagement with Simple Moving Average Crossover: A Mixed-Method Study.Sensors (Basel, Switzerland) · 2024Article
- NeuroBioSense: A multidimensional dataset for neuromarketing analysis.Data in brief · 2024Article
- PPG and EDA dataset collected with Empatica E4 for stress assessment.Data in brief · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In response to challenging circumstances, the human body can experience marked levels of anxiety and distress. To prevent stress-related complications, timely identification of stress symptoms is crucial, necessitating the need for continuous stress monitoring. Wearable devices offer a means of real-time and ongoing data collection, facilitating personalized stress monitoring. Based on our protocol for data pre-processing, this study proposes to analyze signals obtained from the Empatica E4 bracelet using machine-learning algorithms (Random Forest, SVM, and Logistic Regression) to determine the efficacy of the abovementioned techniques in differentiating between stressful and non-stressful situations. Photoplethysmographic and electrodermal activity signals were collected from 29 subjects to extract 27 features which were then fed into three different machine-learning algorithms for binary classification. Using MATLAB after applying the chi-square test and Pearson's correlation coefficient on WEKA for features' importance ranking, the results demonstrated that the Random Forest model has the highest stability (accuracy of 76.5%) using all the features. Moreover, the Random Forest applying the chi-test for feature selection reached consistent results in terms of stress evaluation based on precision, recall, and F1-measure (71%, 60%, 65%, respectively).
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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.