ArticleSensors (Basel, Switzerland)2020
How Laboratory Experiments Can Be Exploited forMonitoring Stress in the Wild: A Bridge BetweenLaboratory and Daily Life.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
The trial behind it
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Who cites it
13 citing papers in PubMed.
- A systematic scoping review identifying effective virtual reality stress tasks inducing physiological reactivity for future wearables validation.Behavior research methods · 2026Review
- Examining the Use of Consumer Wearable Devices and Digital Tools for Stress Measurement in College Students: Scoping Review of Methods.JMIR mHealth and uHealth · 2026Article
- Artificial Intelligence-Based Depression Detection.Sensors (Basel, Switzerland) · 2026Article
- Stress and Emotion Open Access Data: A Review on Datasets, Modalities, Methods, Challenges, and Future Research Perspectives.Journal of healthcare informatics research · 2025Review
- Multilevel and multimodal biobehavioral methods in the study of stress, trauma, and resilience: a systems approach.Journal of neural transmission (Vienna, Austria : 1996) · 2025Review
- A New Algorithm for Estimating a Noiseless, Evenly Sampled, Heart Rate Modulating Signal.Bioengineering (Basel, Switzerland) · 2023Article
- Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance.Sensors (Basel, Switzerland) · 2023Article
- Cross Dataset Analysis for Generalizability of HRV-Based Stress Detection Models.Sensors (Basel, Switzerland) · 2023Article
- Relieving the burden of intensive labeling for stress monitoring in the wild by using semi-supervised learning.Frontiers in psychology · 2023Article
- Examining Stress and Residual Symptoms in Remitted and Partially Remitted Depression Using a Wearable Electrodermal Activity Device: A Pilot Study.IEEE journal of translational engineering in health and medicine · 2023Article
- Emotion and Stress Recognition Related Sensors and Machine Learning Technologies.Sensors (Basel, Switzerland) · 2021Article
- Toward Emotion Recognition From Physiological Signals in the Wild: Approaching the Methodological Issues in Real-Life Data Collection.Frontiers in psychology · 2020Review
- Application of a mobile health data platform for public health surveillance: A case study in stress monitoring and prediction.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Chronic stress leads to poor well-being, and it has effects on life quality and health. Societymay have significant benefits from an automatic daily life stress detection system using unobtrusivewearable devices using physiological signals. However, the performance of these systems is notsufficiently accurate when they are used in unrestricted daily life compared to the systems testedin controlled real-life and laboratory conditions. To test our stress level detection system thatpreprocesses noisy physiological signals, extracts features, and applies machine learning classificationtechniques, we used a laboratory experiment and ecological momentary assessment based datacollection with smartwatches in daily life. We investigated the effect of different labeling techniquesand different training and test environments. In the laboratory environments, we had more controlledsituations, and we could validate the perceived stress from self-reports. When machine learningmodels were trained in the laboratory instead of training them with the data coming from daily life,the accuracy of the system when tested in daily life improved significantly. The subjectivity effectcoming from the self-reports in daily life could be eliminated. Our system obtained higher stresslevel detection accuracy results compared to most of the previous daily life studies.
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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.