ArticleProceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies2020
Evaluating the Reproducibility of Physiological Stress Detection Models.
Article in Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Electrocardiogram-Based Mental Stress Detection Amid Everyday Activities Using Machine Learning: Model Development and Validation Study.Journal of medical Internet research · 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
- Challenges and Strategies of Managing Mental Health Medication in Real-World Practices: Interview Study Among University Students.JMIR human factors · 2026Article
- A nurse-centered edge-server wearable system for real-time monitoring of occupational stress in clinical care.Frontiers in public health · 2026Article
- SHAP-driven insights into multimodal data: behavior phase prediction for industrial safety applications.Scientific reports · 2025Article
- We have liftoff: A discovery study to use artificial intelligence to identify adaptative profiles for future space missions.Physiological reports · 2025Article
- Stress can be detected during emotion-evoking smartphone use: a pilot study using machine learning.Frontiers in digital health · 2025Article
- Detecting Emotional Arousal and Aggressive Driving Using Neural Networks: A Pilot Study Involving Young Drivers in Duluth.Sensors (Basel, Switzerland) · 2024Article
- Machine learning-based detection of acute psychosocial stress from body posture and movements.Scientific reports · 2024Article
- Enhancing Mixed Reality Simulation Training Technology With Real-Time Performance Visualization: Mixed Methods Study With Medical First Responders.JMIR XR and spatial computing · 2024Article
- Building an open-source community to enhance autonomic nervous system signal analysis: DBDP-autonomic.Frontiers in digital health · 2024Article
- Cross Dataset Analysis for Generalizability of HRV-Based Stress Detection Models.Sensors (Basel, Switzerland) · 2023Article
- Explainable stress type classification captures physiologically relevant responses in the Maastricht Acute Stress Test.Frontiers in neuroergonomics · 2023Article
- Detecting Receptivity for mHealth Interventions in the Natural Environment.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 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
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Recent advances in wearable sensor technologies have led to a variety of approaches for detecting physiological stress. Even with over a decade of research in the domain, there still exist many significant challenges, including a near-total lack of reproducibility across studies. Researchers often use some physiological sensors (custom-made or off-the-shelf), conduct a study to collect data, and build machine-learning models to detect stress. There is little effort to test the applicability of the model with similar physiological data collected from different devices, or the efficacy of the model on data collected from different studies, populations, or demographics. This paper takes the first step towards testing reproducibility and validity of methods and machine-learning models for stress detection. To this end, we analyzed data from 90 participants, from four independent controlled studies, using two different types of sensors, with different study protocols and research goals. We started by evaluating the performance of models built using data from one study and tested on data from other studies. Next, we evaluated new methods to improve the performance of stress-detection models and found that our methods led to a consistent increase in performance across all studies, irrespective of the device type, sensor type, or the type of stressor. Finally, we developed and evaluated a clustering approach to determine the
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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.