ArticleData in brief2024
PPG and EDA dataset collected with Empatica E4 for stress assessment.
Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- Driving-Context Classification from Wearable Physiological and Motion Signals During Real-World Driving.Sensors (Basel, Switzerland) · 2026Article
- Heart rate synchrony as an objective biomarker in psychotherapy: a wearable-based pilot study.European archives of psychiatry and clinical neuroscience · 2026Article
- Wearable ECG and PPG for anxiety detection: a translational digital medicine perspective.NPJ digital medicine · 2026Article
- Physiological evaluation of the emotional regulation of patients with hereditary angioedema.BioPsychoSocial medicine · 2026Article
- A Dual In-Person and Remote Assessment Approach to Developing Digital End Points Relevant to Autism and Co-Occurring Conditions: Protocol for a Multisite Observational Study.JMIR research protocols · 2025Article
- Stress and Emotion Open Access Data: A Review on Datasets, Modalities, Methods, Challenges, and Future Research Perspectives.Journal of healthcare informatics research · 2025Review
Corrections and comments
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Authors and funding
5 authors.
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. Wearable devices offer a means of real-time and ongoing data collection, facilitating personalized stress monitoring. Therefore, we collected physiological signals (blood pressure volume and electrodermal activities), using Empatica E4, from 29 subjects. A personalized protocol was developed to cause cognitive, mental, and psychological stressors since they are the ones that can be experienced in working or academic environment. We also propose a pipeline to clean and process these two signals to maximize the quality of further analysis. This study aids in the comprehension of the complex connection between stress and working situations by offering a sizable dataset made up of different physiological data. It additionally enables them to create cutting-edge stress-reduction techniques and improving professional achievement while lessening the negative impact of stress on welfare.
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Registered trials
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