Automation of Stress Recognition Using Subjective or Objective Measures

Exploring the influence of urban art interventions on attraction and wellbeing: an empirical field experiment

While cities are attractive places, brimming with opportunities and possibilities for their inhabitants, they have also been found to have negative consequences, especially on physical and mental health. In a world of ever-growing urban populations, it is important to understand how to make cities healthier and more pleasant places to live. In the present study, we investigated the impact of art as an urban intervention and compared it to the well-known effects of greenery (i.e., plants and vegetation) in an identically framed intervention. Specifically, we looked at how people engage with a Graetzloase (a type of parklet) and its embedding urban environment in terms of visual and spatial attraction as well as wellbeing. The Graetzloase displayed either abstract art or greenery and was placed on two distinct streets that, among other elements, also contained art and greenery. Our field study captured the ongoing experiences during people's exploration of the urban environment by employing mobile eye-trackers and physiological devices. While our findings demonstrated a certain level of visual and spatial attraction towards the Graetzloases, it was not as pronounced as initially anticipated. Nevertheless, our analyses still inform on What decorating element should be placed in a Graetzloase, as well as Where to implement the Graetzloase. Our results suggest that artistic elements are more visually attractive (i.e., they were looked at for longer times) than the greenery, and that both visual and spatial attraction towards the Graetzloases are greatly impacted by the street context. We found that the Art Graetzloase when displayed in a wide street containing greenery elements, is visually more present in the participant's visual field than all the other experimental combinations. The more precise analyses of the participant viewing behavior confirm this trend. Regarding wellbeing, we found no evidence for the impact of street context or the types of decorations in the Graetzloases. Our results establish an initial empirical foundation for the design and placement of not only future parklets but also urban art interventions in general.

A novel multi-model feature generation technique for suicide detection

Automated expert systems (AES) analyzing depression-related content on social media have piqued the interest of researchers. Depression, often linked to suicide, requires early prediction for potential life-saving interventions. In the conventional approach, psychologists conduct patient interviews or administer questionnaires to assess depression levels. However, this traditional method is plagued by limitations. Patients might not feel comfortable disclosing their true feelings to psychologists, and counselors may struggle to accurately predict situations due to limited data. In this context, social media emerges as a potentially valuable resource. Given the widespread use of social media in daily life, individuals often express their nature and mental state through their online posts. AES can efficiently analyze vast amounts of social media content to predict depression levels in individuals at an early stage. This study contributes to this endeavor by proposing an innovative approach for predicting suicide risks using social media content and machine learning techniques. A novel multi-model feature generation technique is employed to enhance the performance of machine learning models. This technique involves the use of a feature extraction method known as term frequency-inverse document frequency (TF-IDF), combined with two machine learning models: logistic regression (LR) and support vector machine (SVM). The proposed technique calculates probabilities for each sample in the dataset, resulting in a new feature set referred to as the probability-based feature set (ProBFS). This ProBFS is compact yet highly correlated with the target classes in the dataset. The utilization of concise and correlated features yields significant outcomes. The SVM model achieves an impressive accuracy score of 0.96 using ProBFS while maintaining a low computational time of 5.63 seconds even when dealing with extensive datasets. Furthermore, a comparison with state-of-the-art approaches is conducted to demonstrate the significance of the proposed method.

Perceived Stress, Salivary Cortisol, and Temperament Traits among Students of Dental Medicine: A Prospective and Interventional Study

Academic stress affects students' psychological and physiological well-being. Dental undergraduate programs are known for their demanding curriculum, leading to significant stress symptoms. The objective was to determine if salivary cortisol levels were higher in students exposed to academic stress, assess the relationship between stress severity/temperament and cortisol values, and explore relaxation technique effects. Salivary cortisol was measured at two time points for all participants: Before exams and during a relaxation period after summer break. A third measurement was conducted for students with high pre-test cortisol levels who received instructions on progressive muscle relaxation (PMR) before subsequent exams. Additionally, participants completed two questionnaires: Perceived Stress Scale (PSS) and Fisher's Temperament Questionnaire. The group analysis based on the PSS indicated that 39 participants reported high stress. Women demonstrated significantly higher stress than men (p = 0.042054). A significant difference in stress levels was observed between director and builder temperament types (p = 0.029276). Cortisol levels showed a significant decrease from the first measurement to the second measurement, and the third measurement after implementing PMR. The grade in the "Dermatovenereology" course correlated with stress level according to the PSS (k = 0.578467). Pre-test cortisol levels correlated with the frequency of using PMR guidelines during winter test periods (k = 0.416138). Stress negatively affects the immune system and poses health risks. Implementing stress reduction techniques in dental/medical education could benefit students and the healthcare system.

Continuous Monitoring of Psychosocial Stress by Non-Invasive Volatilomics

Stress is becoming increasingly commonplace in modern times, making it important to have accurate and effective detection methods. Currently, detection methods such as self-evaluation and clinical questionnaires are subjective and unsuitable for long-term monitoring. There have been significant studies into biomarkers such as HRV, cortisol, electrocardiography, and blood biomarkers, but the use of multiple electrodes for electrocardiography or blood tests is impractical for real-time stress monitoring. To this end, there is a need for non-invasive sensors to monitor stress in real time. This study looks at the possibility of using breath and skin VOC fingerprinting as stress biomarkers. The Trier social stress test (TSST) was used to induce acute stress and HRV, cortisol, and anxiety levels were measured before, during, and after the test. GC-MS and sensor array were used to collect and measure VOCs. A prediction model found eight different stress-related VOCs with an accuracy of up to 78%, and a molecularly capped gold nanoparticle-based sensor revealed a significant difference in breath VOC fingerprints between the two groups. These stress-related VOCs either changed or returned to baseline after the stress induction, suggesting different metabolic pathways at different times. A correlation analysis revealed an association between VOCs and cortisol levels and a weak correlation with either HRV or anxiety levels, suggesting that VOCs may include complementary information in stress detection. This study shows the potential of VOCs as stress biomarkers, paving the way into developing a real-time, objective, non-invasive stress detection tool for well-being and early detection of stress-related diseases.

Mental Stress Detection Using a Wearable In-Ear Plethysmography

This study presents an ear-mounted photoplethysmography (PPG) system that is designed to detect mental stress. Mental stress is a prevalent condition that can negatively impact an individual's health and well-being. Early detection and treatment of mental stress are crucial for preventing related illnesses and maintaining overall wellness. The study used data from 14 participants that were collected in a controlled environment. The participants were subjected to stress-inducing tasks such as the Stroop color-word test and mathematical calculations. The raw PPG signal was then preprocessed and transformed into scalograms using continuous wavelet transform (CWT). A convolutional neural network classifier was then used to classify the transformed signals as stressed or non-stressed. The results of the study show that the PPG system achieved high levels of accuracy (92.04%) and F1-score (90.8%). Furthermore, by adding white Gaussian noise to the raw PPG signals, the results were improved even more, with an accuracy of 96.02% and an F1-score of 95.24%. The proposed ear-mounted device shows great promise as a reliable tool for the early detection and treatment of mental stress, potentially revolutionizing the field of mental health and well-being.

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

Virtual reality (VR) technology offers a great opportunity to explore stress disorder therapies. We created a VR stress training system, which incorporates three highly interactive stressful scenes to elicit stress, and demonstrate the concurrent variations between physiological data (heart rate, electrodermal activity and eye-blink rate) and self-reported stress ratings through a self-designed customized perceived stress questionnaire (SSAI) and wearable devices. Several supervised learning models were rigorously applied to automate stress recognition. Our findings include the evaluations of the VR system by computing Cronbach's alpha ( α = 0.72 ) and Kaiser-Meyer-Olkin (KMO) coefficient ( η = 0.78 ) through a retrospective survey, which were subsequently confirmed as reliable on four aspects (sense of presence, sense of space, sense of immersion and sense of reality) via factor analysis. Additionally, we demonstrate the effectiveness of physiology-based stress level classification (no stress, low stress and high stress) and continuous SSAI score prediction, with accuracy reaching 0.742 by bagging ensemble learning model and goodness-of-fit reaching 0.44 via multivariate stepwise regression. This study provides detailed insight regarding the effect of objective physiological measures on the validation of subjective self-ratings under a novel complex VR stress training system, which stimulates the further investigations of stress disorder recognition and treatment.

Mental Stress Assessment Using Ultra Short Term HRV Analysis Based on Non-Linear Method

Mental stress is on the rise as one of the major health problems in modern society. It is important to detect and manage mental stress to prevent various diseases caused by stress and to maintain a healthy life. The purpose of this paper is to present new heart rate variability (HRV) features based on empirical mode decomposition and to detect acute mental stress through short-term HRV (5 min) and ultra-short-term HRV (under 5 min) analysis. HRV signals were acquired from 74 young police officers using acute stressors, including the Trier Social Stress Test and horror movie viewing, and a total of 26 features, including the proposed IMF energy features and general HRV features, were extracted. A support vector machine (SVM) classification model is used to classify the stress and non-stress states through leave-one-subject-out cross-validation. The classification accuracies of short-term HRV and ultra-short-term HRV analysis are 86.5% and 90.5%, respectively. In the results of ultra-short-term HRV analysis using various time lengths, we suggest the optimal duration to detect mental stress, which can be applied to wearable devices or healthcare systems.

Salivary Cortisol Values and Personality Features of Atopic Dermatitis Patients: A Prospective Study

BACKGROUND

Atopic dermatitis (AD) patients commonly experience psychological stress and impaired psychosocial functioning.

OBJECTIVE

The aim of this study was to compare patients' salivary cortisol levels with AD severity and other associated stress-related psychological measures/parameters.

METHODS

This prospective study analyzed salivary cortisol levels (enzyme-linked immunosorbent assay) in 84 AD patients (42 symptomatic patients and 42 asymptomatic patients). Each subject filled out the Perceived Stress Scale (PSS), Brief Illness Perception Questionnaire, and the Crown-Crisp Experiential Index, which concerns personality features.

RESULTS

Increased cortisol values were found in both groups and were not dependent on disease severity (Scoring Atopic Dermatitis [SCORAD]) and PSS. Patients with severe AD had significantly lower cortisol levels than those with moderate and mild AD (P = 0.042). The PSS levels were not dependent on SCORAD but correlated with the perceived effect of AD on emotional states (Illness Perception Questionnaire 8), personality traits, anxiety, and depression (P < 0.001).

CONCLUSIONS

The severity of perceived stress in AD patients is not adequately measured by salivary cortisol levels nor SCORAD; it does, however, correlate with the impact of AD on patients' emotional states and personality features (anxiety, depression). All AD patients, regardless of disease severity, should be assessed for impacts of stress, and a multidisciplinary approach should address mental wellness.

Human stress classification during public speaking using physiological signals

Public speaking is a common type of social evaluative situation and a significant amount of the population feel uneasy with it. It is of utmost importance to detect public speaking stress so that appropriate action can be taken to minimize its impacts on human health. In this study, a multimodal human stress classification scheme in response to real-life public speaking activity is proposed. Electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG) signals of forty participants are acquired in rest-state and during public speaking activity to divide data into a stressed and non-stressed group. Frequency domain features from EEG and time-domain features from GSR and PPG signals are extracted. The selected set of features from all modalities are fused to classify the stress into two classes. Classification is performed via a leave-one-out cross-validation scheme by using five different classifiers. The highest accuracy of 96.25% is achieved using a support vector machine classifier with radial basis function. Statistical analysis is performed to examine the significance of EEG, GSR, and PPG signals between the two phases of the experiment. Statistical significance is found in certain EEG frequency bands as well as GSR and PPG data recorded before and after public speaking supporting the fact that brain activity, skin conductance, and blood volumetric flow are credible measures of human stress during public speaking activity.

A Review of Biophysiological and Biochemical Indicators of Stress for Connected and Preventive Healthcare

Stress is a known contributor to several life-threatening medical conditions and a risk factor for triggering acute cardiovascular events, as well as a root cause of several social problems. The burden of stress is increasing globally and, with that, is the interest in developing effective stress-monitoring solutions for preventive and connected health, particularly with the help of wearable sensing technologies. The recent development of miniaturized and flexible biosensors has enabled the development of connected wearable solutions to monitor stress and intervene in time to prevent the progression of stress-induced medical conditions. This paper presents a review of the literature on different physiological and chemical indicators of stress, which are commonly used for quantitative assessment of stress, and the associated sensing technologies.

A within-subject comparison of different relaxation therapies in eliciting physiological and psychological changes in young women

Background

Stress reactivity can be different in women compared to men, which might consequently influence disease risk.Stress in women may also generate adverse physiological effects on their offspring during pregnancy or lactation. The objective of this study was to compare the effects of different relaxation interventions on physiological outcomes and perceived relaxation in healthy young women, to assist in identifying the most appropriate intervention(s) for use in a subsequent trial for mothers who deliver prematurely.

Methods

A within-subject study was conducted in 17 women of reproductive age comparing five different relaxation interventions (guided-imagery meditation audio (GIM), music listening (ML), relaxation lighting (RL), GIM+RL, ML+RL), with control (silence/sitting), assigned in random order over a 3–6 week period. Subjective feelings of relaxation (10-point scale), heart rate (HR), systolic and diastolic blood pressure (SBP, DBP), and fingertip temperature (FT) were measured before and after each technique

Results

All interventions significantly increased perceived relaxation and FT, while music also significantly reduced SBP (p < 0.05). Compared to control, HR significantly decreased following GIM (mean difference = 3.2 bpm, p < 0.05), and FT increased (mean difference = 2.2 °C, p < 0.05) and SBP decreased (mean difference = 3.3 mmHg, p < 0.01) following ML. GIM + RL followed by GIM were the most preferred interventions.

Conclusions

Based on preference, simplicity, and the physiological and psychological effects, GIM and ML were identified as the most effective tools for reducing stress and improving relaxation. These techniques warrant further research in larger samples and other populations.

Neuroscience in service research: an overview and discussion of its possibilities

Purpose

The purpose of this paper is to discuss recent developments in neuroscientific methods and demonstrate its potential for the service field. This work is a call to action for more service researchers to adopt promising and increasingly accessible neuro-tools that allow the service field to benefit from neuroscience theories and insights.

Design/methodology/approach

The paper synthesizes key literature from a variety of domains (e.g. neuroscience, consumer neuroscience and organizational neuroscience) to provide an in-depth background to start applying neuro-tools. Specifically, this paper outlines the most important neuro-tools today and discusses their theoretical and empirical value.

Findings

To date, the use of neuro-tools in the service field is limited. This is surprising given the great potential they hold to advance service research. To stimulate the use of neuro-tools in the service area, the authors provide a roadmap to enable neuroscientific service studies and conclude with a discussion on promising areas (e.g. service experience and servicescape) ripe for neuroscientific input.

Originality/value

The paper offers service researchers a starting point to understand the potential benefits of adopting the neuroscientific method and shows their complementarity with traditional service research methods like surveys, experiments and qualitative research. In addition, this paper may also help reviewers and editors to better assess the quality of neuro-studies in service.

Measuring acute stress response through physiological signals: towards a quantitative assessment of stress

Social and medical problems associated with stress are increasing globally and seriously affect mental health and well-being. However, an effective stress-level monitoring method is still not available. This paper presents a quantitative method for monitoring acute stress levels in healthy young people using biomarkers from physiological signals that can be unobtrusively monitored. Two states were induced to 40 volunteers, a basal state generated with a relaxation task and an acute stress state generated by applying a standard stress test that includes five different tasks. Standard psychological questionnaires and biochemical markers were utilized as ground truth of stress levels. A multivariable approach to comprehensively measure the physiological stress response is proposed using stress biomarkers derived from skin temperature, heart rate, and pulse wave signals. Acute physiological stress levels (total-range 0-100 au) were continuously estimated every 1 min showing medians of 29.06 au in the relaxation tasks, while rising from 34.58 to 47.55 au in the stress tasks. Moreover, using the proposed method, five statistically different stress levels induced by the performed tasks were also measured. Results obtained show that, in these experimental conditions, stress can be monitored from unobtrusive biomarkers. Thus, a more general stress monitoring method could be derived based on this approach. Graphical abstract Stress measurements of different healthy young people throughout a Stress Session that includes a pre-relax stage (BL_s), memory test (ST and MT), stress anticipation time (SA), video display (VD) and arithmetic task.