Automated Stress Recognition Using Supervised Learning Classifiers by Interactive Virtual Reality Scenes

Tao, Kuan; Huang, Yuhan; Shen, Yulong; Sun, Lixin

doi:10.1109/tnsre.2022.3192571

Cited by 11 publications

(2 citation statements)

References 53 publications

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“…An increase of EDA could be observed during increased anxiety (e.g. Georgiou et al, 2019;Tao et al, 2022), and ML algorithms are capable of predicting stress in real-life settings (e.g. Hosseini et al, 2022).…”

Section: Relationship Between Eda and Perceived Stress Or Stressrelat...mentioning

confidence: 99%

Wearables measuring electrodermal activity to assess perceived stress in care: a scoping review

Klimek

Mannheim

Schouten

et al. 2023

Acta Neuropsychiatr.

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Background: Chronic stress responses can lead to physical and behavioural health problems, often experienced and observed in the care of people with intellectual disabilities or people with dementia. Electrodermal activity (EDA) is a bio-signal for stress, which can be measured by wearables and thereby support stress management. However, the how, when and to what extent patients and healthcare providers can benefit, is unclear. This study aims to create an overview of available wearables enabling the detection of perceived stress by using EDA. Methods: Following the PRISMA-SCR protocol for scoping reviews, four databases were included in the search of peer-reviewed studies published between 2012-2022, reporting detection of EDA in relation to self-reported stress or stress-related behaviours. Type of wearable, bodily location, research population, context, stressor type and the reported relationship between EDA and perceived stress were extracted. Results: Of the 74 included studies, the majority included healthy subjects in laboratory situations. Field studies and studies using machine learning to predict stress have increased in the last years. EDA is most often measured on the wrist, with offline data processing. Studies predicting perceived stress or stress-related behaviour using EDA features, reported accuracies between 42%-100% with an average of 82.6%. Of these studies, the majority used machine learning. Conclusion: Wearable EDA sensors are promising in detecting perceived stress. Field studies with relevant populations in a health or care context are lacking. Future studies should focus on the application of EDA-measuring wearables in real-life situations to support stress management.

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Section: Relationship Between Eda and Perceived Stress Or Stressrelat...mentioning

confidence: 99%

Wearables measuring electrodermal activity to assess perceived stress in care: a scoping review

Klimek

Mannheim

Schouten

et al. 2023

Acta Neuropsychiatr.

View full text Add to dashboard Cite

show abstract

“…Stress manifests through clinical observations like increased blink and breathing rates, as well as elevated blood pressure and heart rate. Laboratory measurements, including cortisol levels, c-reactive protein (CRP) and fibrinogen, also correlate with stress [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. Additionally, psychometric questionnaires like the Perceived Stress Scale and the General Health Questionnaire provide insights into self-assessment of stress [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Stress-Reducing Interventions on Heart Rate Variability in Cardiovascular Disease: A Systematic Review and Meta-Analysis

El-Malahi,

Mohajeri,

Bäuerle

et al. 2024

Life

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Stress is recognized as a significant trigger and exacerbator of various medical conditions, particularly in the field of cardiovascular disease (CVD). Given that heart rate variability (HRV) offers insight into the functioning of the autonomic nervous system and has been identified as a predictive factor for increased cardiovascular mortality, exploring the correlation between stress and HRV is pertinent. We systematically reviewed trials where researchers investigated the effects of stress-reducing interventions on biomarkers and time-domain/frequency-domain parameters of HRV in CVD. Eligible studies underwent meta-analysis utilizing a random-effects model. The meta-analysis showed overall beneficial effects of stress-reducing interventions on HRV for the standard deviation of Normal-to-Normal intervals (SDNN) in short-term and 24 h assessments, as well as for the low-frequency power (LF) in short-term assessment. Overall effect sizes were notably high and showed significant p-values (short-term SDNN: MD = 6.43, p = 0.01; 24 h SDNN: MD = 10.92, p = 0.004; short-term LF: MD = 160.11, p < 0.001). Our findings highlight the significant impact of stress-reducing interventions in modulating HRV by influencing short-term SDNN and LF parameters, as well as the 24 h assessment of SDNN. These results emphasize the importance of stress-reducing measures in lowering the risk of further progression in CVD and improving patient outcomes.

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Correlating Personality Traits With Acute Stress Responses in Earthquake Simulations: An HRV and RESP Analysis

Li,

Zhu,

Guan

et al. 2024

Stress and Health

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Earthquakes, as significant natural disasters, still cannot be accurately predicted today. Although current earthquake early warning systems can provide alerts several seconds in advance, acute stress responses (ASR) in emergency situations can waste these precious escape seconds. To investigate the correlation between personality and ASR, this study collected the temperament and character of all participants using the Chen Huichang‐60 Temperament Scale and the DISC Personality Inventory. In addition, this study simulated growing earthquakes in an earthquake experience hall, collecting heart rate variability and respiration signal variations throughout the process from subjects. Multivariate analysis of variance (MANOVA) and Toeplitz Inverse Covariance‐Based Clustering methods were used to analyse the differences and connections between them. Furthermore, this study employed a deep learning model that combines Convolutional Neural Networks (CNN) and Long Short‐Term Memory (LSTM) to predict ASR across personalities. This model used datasets from the majority dataset of a certain personality and a single participant, respectively, and showed different performance. The results are as follows. After categorising participants based on personality test results, MANOVA revealed significant differences between the personality groups Influence‐Choleric and Influence‐Sanguine (p = 0.001), Influence‐Phlegmatic and Steadiness‐Sanguine (p = 0.023), Influence‐Sanguine and Steadiness‐Sanguine (p < 0.001) and Influence‐Sanguine and Steadiness‐Phlegmatic (p < 0.001), as well as across different earthquake stages (p < 0.01). The clustering method quantified stress responses over time for different personalities and labelled ASR levels for use in supervised learning. Ultimately, the CNN‐LSTM model performed predictions of ASR using both personality and individual datasets, achieving the AUC of 0.795 and 0.72, demonstrating better prediction and classification effectiveness with the former. This study provides a new personality‐based method for earthquake stress management, creating possibilities for longitudinal stress research and prediction. It aids the general public in comprehending their own acute stress and allows authorities and communities to make practical, efficient disaster evacuation plans based on the overall situation of public ASR.

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Automated Stress Recognition Using Supervised Learning Classifiers by Interactive Virtual Reality Scenes

Cited by 11 publications

References 53 publications

Wearables measuring electrodermal activity to assess perceived stress in care: a scoping review

Wearables measuring electrodermal activity to assess perceived stress in care: a scoping review

The Effect of Stress-Reducing Interventions on Heart Rate Variability in Cardiovascular Disease: A Systematic Review and Meta-Analysis

Correlating Personality Traits With Acute Stress Responses in Earthquake Simulations: An HRV and RESP Analysis

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