ArticleACM transactions on computing for healthcare2020
Continuous Detection of Physiological Stress with Commodity Hardware.
Article in ACM transactions on computing for healthcare, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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
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
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Chest-Worn Sensors in Cardiac Assessment: Technologies, Advantages, and Limitations.Sensors (Basel, Switzerland) · 2025Pooled it
- Machine Learning Frameworks for Wearable-Based Stress Modeling in Naturalistic Settings: Scoping Review.JMIR mHealth and uHealth · 2026Article
- 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
- Current challenges and opportunities in active and passive data collection for mobile health sensing: a scoping review.JAMIA open · 2025Review
- Detecting Emotional Arousal and Aggressive Driving Using Neural Networks: A Pilot Study Involving Young Drivers in Duluth.Sensors (Basel, Switzerland) · 2024Article
- Lessons From 3 Longitudinal Sensor-Based Human Behavior Assessment Field Studies and an Approach to Support Stakeholder Management: Content Analysis.Journal of medical Internet research · 2024Article
- Perceived stress and associations between physical activity, sedentary time, and interstitial glucose in healthy adolescents.Physiology & behavior · 2024Article
- Building an open-source community to enhance autonomic nervous system signal analysis: DBDP-autonomic.Frontiers in digital health · 2024Article
- Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices.AIMS neuroscience · 2024Review
- Heart Rate Variability in Concussed College Athletes: Follow-Up Study and Biological Sex Differences.Brain sciences · 2023Article
- Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- Early Life Stress Detection Using Physiological Signals and Machine Learning Pipelines.Biology · 2023Article
- Explainable stress type classification captures physiologically relevant responses in the Maastricht Acute Stress Test.Frontiers in neuroergonomics · 2023Article
- Applications and Techniques for Fast Machine Learning in Science.Frontiers in big data · 2022Review
- Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals.Sensors (Basel, Switzerland) · 2021Article
- When Do Drivers Interact with In-Vehicle Well-being Interventions? An Exploratory Analysis of a Longitudinal Study on Public Roads.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2021Article
- Workshop on Implementation Science and Digital Therapeutics for Behavioral Health.JMIR mental health · 2021Article
- Article
- Evaluating the Reproducibility of Physiological Stress Detection Models.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2020Article
- Workshop on the Development and Evaluation of Digital Therapeutics for Health Behavior Change: Science, Methods, and Projects.JMIR mental health · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Timely detection of an individual's stress level has the potential to improve stress management, thereby reducing the risk of adverse health consequences that may arise due to mismanagement of stress. Recent advances in wearable sensing have resulted in multiple approaches to detect and monitor stress with varying levels of accuracy. The most accurate methods, however, rely on clinical-grade sensors to measure physiological signals; they are often bulky, custom made, and expensive, hence limiting their adoption by researchers and the general public. In this article, we explore the viability of commercially available off-the-shelf sensors for stress monitoring. The idea is to be able to use cheap, nonclinical sensors to capture physiological signals and make inferences about the wearer's stress level based on that data. We describe a system involving a popular off-the-shelf heart rate monitor, the Polar H7; we evaluated our system with 26 participants in both a controlled lab setting with three well-validated stress-inducing stimuli and in free-living field conditions. Our analysis shows that using the off-the-shelf sensor alone, we were able to detect stressful events with an
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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.