ArticleBiosensors2022
Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network.
Article in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.
- Human-Centric Cognitive State Recognition Using Physiological Signals: A Systematic Review of Machine Learning Strategies Across Application Domains.Sensors (Basel, Switzerland) · 2025Pooled it
- A Hybrid CNN-SVM Approach for ECG-Based Multi-Class Differential Diagnosis of PTSD, Depression, and Panic Attack.Biosensors · 2026Article
- Monitoring and modulating interconnected physiological systems in space using portable closed-loop technologies.Frontiers in network physiology · 2026Article
- Self-Regulation Mediates the Relationship Between Stress and Quality of Life in Shift-Working Healthcare Professionals: Behavioral Clustering Insights.European journal of investigation in health, psychology and education · 2025Article
- A deep learning approach to stress recognition through multimodal physiological signal image transformation.Scientific reports · 2025Article
- Photoplethysmography-based HRV analysis and machine learning for real-time stress quantification in mental health applications.APL bioengineering · 2025Article
- A Comparative Study of Machine Learning and Deep Learning Models for Automatic Parkinson's Disease Detection from Electroencephalogram Signals.Diagnostics (Basel, Switzerland) · 2025Article
- Extending Anxiety Detection from Multimodal Wearables in Controlled Conditions to Real-World Environments.Sensors (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses.Diagnostics (Basel, Switzerland) · 2025Review
- Investigating lightweight and interpretable machine learning models for efficient and explainable stress detection.Frontiers in digital health · 2025Article
- Article
- Enhancing Mixed Reality Simulation Training Technology With Real-Time Performance Visualization: Mixed Methods Study With Medical First Responders.JMIR XR and spatial computing · 2024Article
- Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition.PloS one · 2024Article
- Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest Model.Sensors (Basel, Switzerland) · 2023Article
- Wrapper-based deep feature optimization for activity recognition in the wearable sensor networks of healthcare systems.Scientific reports · 2023Article
- Relieving the burden of intensive labeling for stress monitoring in the wild by using semi-supervised learning.Frontiers in psychology · 2023Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The human body is designed to experience stress and react to it, and experiencing challenges causes our body to produce physical and mental responses and also helps our body to adjust to new situations. However, stress becomes a problem when it continues to remain without a period of relaxation or relief. When a person has long-term stress, continued activation of the stress response causes wear and tear on the body. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and thus is deeply detrimental to our health. Previous researchers have performed a lot of work regarding mental stress, using mainly machine-learning-based approaches. However, most of the methods have used raw, unprocessed data, which cause more errors and thereby affect the overall model performance. Moreover, corrupt data values are very common, especially for wearable sensor datasets, which may also lead to poor performance in this regard. This paper introduces a deep-learning-based method for mental stress detection by encoding time series raw data into Gramian Angular Field images, which results in promising accuracy while detecting the stress levels of an individual. The experiment has been conducted on two standard benchmark datasets, namely WESAD (wearable stress and affect detection) and SWELL. During the studies, testing accuracies of 94.8% and 99.39% are achieved for the WESAD and SWELL datasets, respectively. For the WESAD dataset, chest data are taken for the experiment, including the data of sensor modalities such as three-axis acceleration (ACC), electrocardiogram (ECG), body temperature (TEMP), respiration (RESP), etc.
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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.