Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review

Ultra-short-term stress measurement using RGB camera-based remote photoplethysmography with reduced effects of Individual differences in heart rate

Stress is linked to health problems, increasing the need for immediate monitoring. Traditional methods like electrocardiograms or contact photoplethysmography require device attachment, causing discomfort, and ultra-short-term stress measurement research remains inadequate. This paper proposes a method for ultra-short-term stress monitoring using remote photoplethysmography (rPPG). Previous predictions of ultra-short-term stress have typically used pulse rate variability (PRV) features derived from time-segmented heart rate data. However, PRV varies at the same stress levels depending on heart rates, necessitating a new method to account for these differences. This study addressed this by segmenting rPPG data based on normal-to-normal intervals (NNIs), converted from peak-to-peak intervals, to predict ultra-short-term stress indices. We used NNI counts corresponding to average durations of 10, 20, and 30 s (13, 26, and 39 NNIs) to extract PRV features, predicting the Baevsky stress index through regressors. The Extra Trees Regressor achieved R2 scores of 0.6699 for 13 NNIs, 0.8751 for 26 NNIs, and 0.9358 for 39 NNIs, surpassing the time-segmented approach, which yielded 0.4162, 0.6528, and 0.7943 for 10, 20, and 30-s intervals, respectively. These findings demonstrate that using NNI counts for ultra-short-term stress prediction improves accuracy by accounting for individual bio-signal variations.

LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network

The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical signals using electrodes, which illustrate the functioning of cardiac muscles during contraction and relaxation phases. ECG is instrumental in identifying abnormal cardiac activity, heart attacks, and various cardiac conditions. Arrhythmia detection, a critical aspect of ECG analysis, entails accurately classifying heartbeats. However, ECG signal analysis demands a high level of expertise, introducing the possibility of human errors in interpretation. Hence, there is a clear need for robust automated detection techniques. Recently, numerous methods have emerged for arrhythmia detection from ECG signals. In our research, we developed a novel one-dimensional deep neural network technique called linear deep convolutional neural network (LDCNN) to identify arrhythmias from ECG signals. We compare our suggested method with several state-of-the-art algorithms for arrhythmia detection. We evaluate our methodology using benchmark datasets, including the PTB Diagnostic ECG and MIT-BIH Arrhythmia databases. Our proposed method achieves high accuracy rates of 99.24% on the PTB Diagnostic ECG dataset and 99.38% on the MIT-BIH Arrhythmia dataset.

One-Channel Wearable Mental Stress State Monitoring System

Assessments of stress can be performed using physiological signals, such as electroencephalograms (EEGs) and galvanic skin response (GSR). Commercialized systems that are used to detect stress with EEGs require a controlled environment with many channels, which prohibits their daily use. Fortunately, there is a rise in the utilization of wearable devices for stress monitoring, offering more flexibility. In this paper, we developed a wearable monitoring system that integrates both EEGs and GSR. The novelty of our proposed device is that it only requires one channel to acquire both physiological signals. Through sensor fusion, we achieved an improved accuracy, lower cost, and improved ease of use. We tested the proposed system experimentally on twenty human subjects. We estimated the power spectrum of the EEG signals and utilized five machine learning classifiers to differentiate between two levels of mental stress. Furthermore, we investigated the optimum electrode location on the scalp when using only one channel. Our results demonstrate the system's capability to classify two levels of mental stress with a maximum accuracy of 70.3% when using EEGs alone and 84.6% when using fused EEG and GSR data. This paper shows that stress detection is reliable using only one channel on the prefrontal and ventrolateral prefrontal regions of the brain.

Effect of stress-based interventions on the quality of life of people with an intellectual disability and their caregivers

PURPOSE

People with intellectual disabilities often show challenging behaviour, which can manifest itself in self-harm or aggression towards others. Real-time monitoring of stress in clients with challenging behaviour can help caregivers to promptly deploy interventions to prevent escalations, ultimately to improve the quality of life of client and caregiver. This study aimed to assess the impact of real-time stress monitoring with HUME, and the subsequent interventions deployed by the care team, on stress levels and quality of life.

MATERIALS AND METHODS

Real-time stress monitoring was used in 41 clients with intellectual disabilities in a long-term care setting over a period of six months. Stress levels were determined at the start and during the deployment of the stress monitoring system. The quality of life of the client and caregiver was measured with the Outcome Rating Scale at the start and at three months of use.

RESULTS

The results showed that the HUME-based interventions resulted in a stress reduction. The perceived quality of life was higher after three months for both the clients and caregivers. Furthermore, interventions to provide proximity were found to be most effective in reducing stress and increasing the client's quality of life.

CONCLUSIONS

The study demonstrates that real-time stress monitoring with the HUME and the following interventions were effective. There was less stress in clients with an intellectual disability and an increase in the perceived quality of life. Future larger and randomized controlled studies are needed to confirm these findings.

How does day-to-day stress appraisal relate to coping among office workers in academia? An ecological momentary assessment study

Existing literature indicates that academic staff experience increasing levels of work stress. This study investigated associations between day-to-day threat and challenge appraisal and day-to-day problem-focused coping, emotion-focused coping, and seeking social support among academic office workers. This study is based on an Ecological Momentary Assessment (EMA) design with a 15-working day data collection period utilising our self-developed STRAW smartphone application. A total of 55 office workers from academic institutions in Belgium (n = 29) and Slovenia (n = 26) were included and 3665 item measurements were analysed. Participants were asked approximately every 90 min about their appraisal of stressful events (experienced during the working day) and their coping styles. For data analysis, we used an unstructured covariance matrix in our linear mixed models. Challenge appraisal predicted problem-focused coping and threat appraisal predicted emotion-focused coping. Our findings suggest an association between threat appraisal as well as challenge appraisal and seeking social support. Younger and female workers chose social support more often as a coping style. While working from home, participants were less likely to seek social support. The findings of our EMA study confirm previous research on the relationship between stress appraisal and coping with stress. Participants reported seeking social support less while working from home compared to working at the office, making the work location an aspect that deserves further research.

Applications of artificial intelligence-machine learning for detection of stress: a critical overview

Psychological distress is a major contributor to human physiology and pathophysiology, and it has been linked to several conditions, such as auto-immune diseases, metabolic syndrome, sleep disorders, and suicidal thoughts and inclination. Therefore, early detection and management of chronic stress is crucial for the prevention of several diseases. Artificial intelligence (AI) and Machine Learning (ML) have promoted a paradigm shift in several areas of biomedicine including diagnosis, monitoring, and prognosis of disease. Here, our review aims to present some of the AI and ML applications for solving biomedical issues related to psychological stress. We provide several lines of evidence from previous studies highlighting that AI and ML have been able to predict stress and detect the brain normal states vs. abnormal states (notably, in post-traumatic stress disorder (PTSD)) with accuracy around 90%. Of note, AI/ML-driven technology applied to identify ubiquitously present stress exposure may not reach its full potential, unless future analytics focus on detecting prolonged distress through such technology instead of merely assessing stress exposure. Moving forward, we propose that a new subcategory of AI methods called Swarm Intelligence (SI) can be used towards detecting stress and PTSD. SI involves ensemble learning techniques to efficiently solve a complex problem, such as stress detection, and it offers particular strength in clinical settings, such as privacy preservation. We posit that AI and ML approaches will be beneficial for the medical and patient community when applied to predict and assess stress levels. Last, we encourage additional research to bring AI and ML into the standard clinical practice for diagnostics in the not-too-distant future.

Real-time stress detection based on artificial intelligence for people with an intellectual disability

People with severe intellectual disabilities (ID) could have difficulty expressing their stress which may complicate timely responses from caregivers. The present study proposes an automatic stress detection system that can work in real-time. The system uses wearable sensors that record physiological signals in combination with machine learning to detect physiological changes related to stress. Four experiments were conducted to assess if the system could detect stress in people with and without ID. Three experiments were conducted with people without ID (n = 14, n = 18, and n = 48), and one observational study was done with people with ID (n = 12). To analyze if the system could detect stress, the performance of random, general, and personalized models was evaluated. The mixed ANOVA found a significant effect for model type, F(2, 134) = 116.50, p < .001. Additionally, the post-hoc t-tests found that the personalized model for the group with ID performed better than the random model, t(11) = 9.05, p < .001. The findings suggest that the personalized model can detect stress in people with and without ID. A larger-scale study is required to validate the system for people with ID.

Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices

Stress has emerged as a prominent and multifaceted health concern in contemporary society, manifesting detrimental effects on individuals' physical and mental health and well-being. The ability to accurately predict stress levels in real time holds significant promise for facilitating timely interventions and personalized stress management strategies. The increasing incidence of stress-related physical and mental health issues highlights the importance of thoroughly understanding stress prediction mechanisms. Given that stress is a contributing factor to a wide array of mental and physical health problems, objectively assessing stress is crucial for behavioral and physiological studies. While numerous studies have assessed stress levels in controlled environments, the objective evaluation of stress in everyday settings still needs to be explored, primarily due to contextual factors and limitations in self-report adherence. This short review explored the emerging field of real-time stress prediction, focusing on utilizing physiological data collected by wearable devices. Stress was examined from a comprehensive standpoint, acknowledging its effects on both physical and mental well-being. The review synthesized existing research on the development and application of stress prediction models, underscoring advancements, challenges, and future directions in this rapidly evolving domain. Emphasis was placed on examining and critically evaluating the existing research and literature on stress prediction, physiological data analysis, and wearable devices for stress monitoring. The synthesis of findings aimed to contribute to a better understanding of the potential of wearable technology in objectively assessing and predicting stress levels in real time, thereby informing the design of effective interventions and personalized stress management approaches.

Stress appraisal in the workplace and its associations with productivity and mood: Insights from a multimodal machine learning analysis

Previous studies have primarily focused on predicting stress arousal, encompassing physiological, behavioral, and psychological responses to stressors, while neglecting the examination of stress appraisal. Stress appraisal involves the cognitive evaluation of a situation as stressful or non-stressful, and as a threat/pressure or a challenge/opportunity. In this study, we investigated several research questions related to the association between states of stress appraisal (i.e., boredom, eustress, coexisting eustress-distress, distress) and various factors such as stress levels, mood, productivity, physiological and behavioral responses, as well as the most effective ML algorithms and data signals for predicting stress appraisal. The results support the Yerkes-Dodson law, showing that a moderate stress level is associated with increased productivity and positive mood, while low and high levels of stress are related to decreased productivity and negative mood, with distress overpowering eustress when they coexist. Changes in stress appraisal relative to physiological and behavioral features were examined through the lenses of stress arousal, activity engagement, and performance. An XGBOOST model achieved the best prediction accuracies of stress appraisal, reaching 82.78% when combining physiological and behavioral features and 79.55% using only the physiological dataset. The small accuracy difference of 3% indicates that physiological data alone may be adequate to accurately predict stress appraisal, and the feature importance results identified electrodermal activity, skin temperature, and blood volume pulse as the most useful physiologic features. Implementing these models within work environments can serve as a foundation for designing workplace policies, practices, and stress management strategies that prioritize the promotion of eustress while reducing distress and boredom. Such efforts can foster a supportive work environment to enhance employee well-being and productivity.

Methods and evaluation of physiological measurements with acoustic stimuli-a systematic review

Objective. The detection of psychological loads, such as stress reactions, is receiving greater attention and social interest, as stress can have long-term effects on health O'Connor, Thayer and Vedhara (2021Ann. Rev. Psychol.72, 663-688). Acoustic stimuli, especially noise, are investigated as triggering factors. The application of physiological measurements in the detection of psychological loads enables the recording of a further quantitative dimension that goes beyond purely perceptive questionnaires. Thus, unconscious reactions to acoustic stimuli can also be captured. The numerous physiological signals and possible experimental designs with acoustic stimuli may quickly lead to a challenging implementation of the study and an increased difficulty in reproduction or comparison between studies. An unsuitable experimental design or processing of the physiological data may result in conclusions about psychological loads that are not valid anymore.Approach. The systematic review according to the preferred reporting items for systematic reviews and meta-analysis standard presented here is therefore intended to provide guidance and a basis for further studies in this field. For this purpose, studies were identified in which the participants' short-term physiological responses to acoustic stimuli were investigated in the context of a listening test in a laboratory study.Main Results. A total of 37 studies met these criteria and data items were analysed in terms of the experimental design (studied psychological load, independent variables/acoustic stimuli, participants, playback, scenario/context, duration of test phases, questionnaires for perceptual comparison) and the physiological signals (measures, calculated features, systems, data processing methods, data analysis methods, results). The overviews show that stress is the most studied psychological load in response to acoustic stimuli. An ECG/PPG system and the measurement of skin conductance were most frequently used for the detection of psychological loads. A critical aspect is the numerous different methods of experimental design, which prevent comparability of the results. In the future, more standardized methods are needed to achieve more valid analyses of the effects of acoustic stimuli.

Predicting Office Workers' Productivity: A Machine Learning Approach Integrating Physiological, Behavioral, and Psychological Indicators

This research pioneers the application of a machine learning framework to predict the perceived productivity of office workers using physiological, behavioral, and psychological features. Two approaches were compared: the baseline model, predicting productivity based on physiological and behavioral characteristics, and the extended model, incorporating predictions of psychological states such as stress, eustress, distress, and mood. Various machine learning models were utilized and compared to assess their predictive accuracy for psychological states and productivity, with XGBoost emerging as the top performer. The extended model outperformed the baseline model, achieving an R2 of 0.60 and a lower MAE of 10.52, compared to the baseline model's R2 of 0.48 and MAE of 16.62. The extended model's feature importance analysis revealed valuable insights into the key predictors of productivity, shedding light on the role of psychological states in the prediction process. Notably, mood and eustress emerged as significant predictors of productivity. Physiological and behavioral features, including skin temperature, electrodermal activity, facial movements, and wrist acceleration, were also identified. Lastly, a comparative analysis revealed that wearable devices (Empatica E4 and H10 Polar) outperformed workstation addons (Kinect camera and computer-usage monitoring application) in predicting productivity, emphasizing the potential utility of wearable devices as an independent tool for assessment of productivity. Implementing the model within smart workstations allows for adaptable environments that boost productivity and overall well-being among office workers.

Identification of heart rate change during the teaching process

Internet of Things (IoT) technology can be used in many areas of everyday life. The objective of this paper is to obtain physiological functions in a non-invasive manner using commonly available IoT devices. The aim of the research is to point out the possibility of using physiological functions as an identifier of changes in students' level of arousal during the teaching process. The motivation of the work is to find a correlation between the change in heart rate, the student's level of arousal and the student's partial and final learning results. The research was focused on the collection of physiological data, namely heart rate and the evaluation of these data in the context of identification of arousal during individual teaching activities of the teaching process. The experiment was carried out during the COVID-19 pandemic via distance learning. During the teaching process, individual activities were recorded in time and HR was assigned to them. The benefit of the research is the proposed methodology of the system, which can identify changes in students' arousal in order to increase the efficiency of the teaching process. Based on the results of the designed system, they could also alert teachers who should be able to modify their teaching style in specific situations so that it is suitable for students and provides a basis for better teaching and understanding of educational materials. The presented methodology will be able to guarantee an increase in the success of the teaching process itself in terms of students' understanding of the teaching materials.

Reducing Stress with Yoga: A Systematic Review Based on Multimodal Biosignals

Stress is an enormous concern in our culture because it is the root cause of many health issues. Yoga asanas and mindfulness-based practices are becoming increasingly popular for stress management; nevertheless, the biological effect of these practices on stress reactivity is still a research domain. The purpose of this review is to emphasize various biosignals that reflect stress reduction through various yoga-based practices. A comprehensive synthesis of numerous prior investigations in the existing literature was conducted. These investigations undertook a thorough examination of numerous biosignals. Various features are extracted from these signals, which are further explored to reflect the effectiveness of yoga practice in stress reduction. The multifaceted character of stress and the extensive research undertaken in this field indicate that the proposed approach would rely on multiple modalities. The notable growth of the body of literature pertaining to prospective yoga processes is deserving of attention; nonetheless, there exists a scarcity of research undertaken on these mechanisms. Hence, it is recommended that future studies adopt more stringent yoga methods and ensure the incorporation of suitable participant cohorts.

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.

Measurement of stress-induced sympathetic nervous activity using multi-wavelength PPG

The onset of stress triggers sympathetic arousal (SA), which causes detectable changes to physiological parameters such as heart rate, blood pressure, dilation of the pupils and sweat release. The objective quantification of SA has tremendous potential to prevent and manage psychological disorders. Photoplethysmography (PPG), a non-invasive method to measure skin blood flow changes, has been used to estimate SA indirectly. However, the impact of various wavelengths of the PPG signal has not been investigated for estimating SA. In this study, we explore the feasibility of using various statistical and nonlinear features derived from peak-to-peak (AC) values of PPG signals of different wavelengths (green, blue, infrared and red) to estimate stress-induced changes in SA and compare their performances. The impact of two physical stressors: and Hand Grip are studied on 32 healthy individuals. Linear (Mean, s.d.) and nonlinear (Katz, Petrosian, Higuchi, SampEn, TotalSampEn) features are extracted from the PPG signal's AC amplitudes to identify the onset, continuation and recovery phases of those stressors. The results show that the nonlinear features are the most promising in detecting stress-induced sympathetic activity. TotalSampEn feature was capable of detecting stress-induced changes in SA for all wavelengths, whereas other features (Petrosian, AvgSampEn) are significant (AUC ≥ 0.8) only for IR and Red wavelengths. The outcomes of this study can be used to make device design decisions as well as develop stress detection algorithms.

Video stimuli suitable for stress estimation based on biosignals

Stress can cause mental disorders such as depression and anxiety disorders. To detect such mental disorders at an early stage, it is necessary to detect stress accurately. One of the effective methods for this purpose is observing changes in biological signals caused by sensory stimuli such as video presentation. This study aims to identify effective video stimuli for stress estimation. We hypothesize that the emotional state evoked by the video stimuli influences the accuracy of stress estimation. To test this hypothesis, we utilized an open video dataset consisting of 444 responses on an emotion scale (valence and arousal) as emotional stimuli. Ninety videos were divided into emotion subsets based on the emotion scale for each video, and biological signals were measured when each video was presented to the subjects. Machine learning models were constructed for each subset, and the prediction errors were compared. The results showed that the prediction error was lower for the high valence and high arousal subsets than for the others. These results suggest that high-valence or high-arousal videos effectively estimate stress.

Attention Aware Deep Learning Approaches for an Efficient Stress Classification Model

In today's world, stress is a major factor for various diseases in modern societies which affects the day-to-day activities of human beings. The measurement of stress is a contributing factor for governments and societies that impacts the quality of daily lives. The strategy of stress monitoring systems requires an accurate stress classification technique which is identified via the reactions of the body to regulate itself to changes within the environment through mental and emotional responses. Therefore, this research proposed a novel deep learning approach for the stress classification system. In this paper, we presented an Enhanced Long Short-Term Memory(E-LSTM) based on the feature attention mechanism that focuses on determining and categorizing the stress polarity using sequential modeling and word-feature seizing. The proposed approach integrates pre-feature attention in E-LSTM to identify the complicated relationship and extract the keywords through an attention layer for stress classification. This research has been evaluated using a selected dataset accessed from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze health-related stress data. Statistical performance of the developed approach was analyzed based on the nine features of stress detection, and we compared the effectiveness of the developed approach with other different stress classification approaches. The experimental results shown that the developed approach obtained accuracy, precision, recall and a F1-score of 75.54%, 74.26%, 72.99% and 74.58%, respectively. The feature attention mechanism-based E-LSTM approach demonstrated superior performance in stress detection classification when compared to other classification methods including naïve Bayesian, SVM, deep belief network, and standard LSTM. The results of this study demonstrated the efficiency of the proposed approach in accurately classifying stress detection, particularly in stress monitoring systems where it is expected to be effective for stress prediction.

Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review

The development of technology, such as the Internet of Things and artificial intelligence, has significantly advanced many fields of study. Animal research is no exception, as these technologies have enabled data collection through various sensing devices. Advanced computer systems equipped with artificial intelligence capabilities can process these data, allowing researchers to identify significant behaviors related to the detection of illnesses, discerning the emotional state of the animals, and even recognizing individual animal identities. This review includes articles in the English language published between 2011 and 2022. A total of 263 articles were retrieved, and after applying inclusion criteria, only 23 were deemed eligible for analysis. Sensor fusion algorithms were categorized into three levels: Raw or low (26%), Feature or medium (39%), and Decision or high (34%). Most articles focused on posture and activity detection, and the target species were primarily cows (32%) and horses (12%) in the three levels of fusion. The accelerometer was present at all levels. The findings indicate that the study of sensor fusion applied to animals is still in its early stages and has yet to be fully explored. There is an opportunity to research the use of sensor fusion for combining movement data with biometric sensors to develop animal welfare applications. Overall, the integration of sensor fusion and machine learning algorithms can provide a more in-depth understanding of animal behavior and contribute to better animal welfare, production efficiency, and conservation efforts.

Experimental Verification of the Possibility of Reducing Photoplethysmography Measurement Time for Stress Index Calculation

Stress is a direct or indirect cause of reduced work efficiency in daily life. It can damage physical and mental health, leading to cardiovascular disease and depression. With increased interest and awareness of the risks of stress in modern society, there is a growing demand for quick assessment and monitoring of stress levels. Traditional ultra-short-term stress measurement classifies stress situations using heart rate variability (HRV) or pulse rate variability (PRV) information extracted from electrocardiogram (ECG) or photoplethysmography (PPG) signals. However, it requires more than one minute, making it difficult to monitor stress status in real-time and accurately predict stress levels. In this paper, stress indices were predicted using PRV indices acquired at different lengths of time (60 s, 50 s, 40 s, 30 s, 20 s, 10 s, and 5 s) for the purpose of real-time stress monitoring. Stress was predicted with Extra Tree Regressor, Random Forest Regressor, and Gradient Boost Regressor models using a valid PRV index for each data acquisition time. The predicted stress index was evaluated using an R2 score between the predicted stress index and the actual stress index calculated from one minute of the PPG signal. The average R2 score of the three models by the data acquisition time was 0.2194 at 5 s, 0.7600 at 10 s, 0.8846 at 20 s, 0.9263 at 30 s, 0.9501 at 40 s, 0.9733 at 50 s, and 0.9909 at 60 s. Thus, when stress was predicted using PPG data acquired for 10 s or more, the R2 score was confirmed to be over 0.7.

A clinical and technical methodological review on stress detection and sleep quality prediction in an academic environment

BACKGROUND

Mental health in recent times is a much talked about topic and its effects on the sleep health of the students are said to result in long-term health issues if not identified and resolved. Students who are subjected to psychological stress have often been reported to have lower sleep quality which together has affected the academic performance of the students.

OBJECTIVE

While stress has its adverse effect on students'quality of sleep, an effort is also made to identify standard techniques and tools to automatically assess stress levels and sleep quality in a non-invasive environment among students only. This article mainly focuses on the Clinical and technical methodology employed in stress level detection and sleep quality prediction among students.

METHODS

This study was conducted by examining all research studies conducted in the past with respect to students in an academic setting from year 2000 to early 2022. The papers under study where finalised based on different methodologies involved in stress level detection and sleep quality prediction considering both in unimodal and multimodal measurements.

RESULTS

While questionnaires and physiological signals are used as a standard measuring tool, it is mostly used in a unimodal environment to measure students' mental stress or sleep quality in academic settings.

CONCLUSION

This paper describes in detail the clinical aspect of the association between mental stress, sleep quality, and academic performance in students followed by technical aspects to analyse the stress levels and sleep quality both qualitatively and quantitatively in an academic environment.

Individual-level interventions for reducing occupational stress in healthcare workers

BACKGROUND

Healthcare workers can suffer from work-related stress as a result of an imbalance of demands, skills and social support at work. This may lead to stress, burnout and psychosomatic problems, and deterioration of service provision. This is an update of a Cochrane Review that was last updated in 2015, which has been split into this review and a review on organisational-level interventions.  OBJECTIVES: To evaluate the effectiveness of stress-reduction interventions targeting individual healthcare workers compared to no intervention, wait list, placebo, no stress-reduction intervention or another type of stress-reduction intervention in reducing stress symptoms.  SEARCH METHODS: We used the previous version of the review as one source of studies (search date: November 2013). We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, PsycINFO, CINAHL, Web of Science and a trials register from 2013 up to February 2022.

SELECTION CRITERIA

We included randomised controlled trials (RCT) evaluating the effectiveness of stress interventions directed at healthcare workers. We included only interventions targeted at individual healthcare workers aimed at reducing stress symptoms.  DATA COLLECTION AND ANALYSIS: Review authors independently selected trials for inclusion, assessed risk of bias and extracted data. We used standard methodological procedures expected by Cochrane. We categorised interventions into ones that: 1. focus one's attention on the (modification of the) experience of stress (thoughts, feelings, behaviour);  2. focus one's attention away from the experience of stress by various means of psychological disengagement (e.g. relaxing, exercise);  3. alter work-related risk factors on an individual level; and ones that 4. combine two or more of the above.  The crucial outcome measure was stress symptoms measured with various self-reported questionnaires such as the Maslach Burnout Inventory (MBI), measured at short term (up to and including three months after the intervention ended), medium term (> 3 to 12 months after the intervention ended), and long term follow-up (> 12 months after the intervention ended).  MAIN RESULTS: This is the second update of the original Cochrane Review published in 2006, Issue 4. This review update includes 89 new studies, bringing the total number of studies in the current review to 117 with a total of 11,119 participants randomised.  The number of participants per study arm was ≥ 50 in 32 studies. The most important risk of bias was the lack of blinding of participants.  Focus on the experience of stress versus no intervention/wait list/placebo/no stress-reduction intervention Fifty-two studies studied an intervention in which one's focus is on the experience of stress. Overall, such interventions may result in a reduction in stress symptoms in the short term (standardised mean difference (SMD) -0.37, 95% confidence interval (CI) -0.52 to -0.23; 41 RCTs; 3645 participants; low-certainty evidence) and medium term (SMD -0.43, 95% CI -0.71 to -0.14; 19 RCTs; 1851 participants; low-certainty evidence). The SMD of the short-term result translates back to 4.6 points fewer on the MBI-emotional exhaustion scale (MBI-EE, a scale from 0 to 54). The evidence is very uncertain (one RCT; 68 participants, very low-certainty evidence) about the long-term effect on stress symptoms of focusing one's attention on the experience of stress. Focus away from the experience of stress versus no intervention/wait list/placebo/no stress-reduction intervention  Forty-two studies studied an intervention in which one's focus is away from the experience of stress. Overall, such interventions may result in a reduction in stress symptoms in the short term (SMD -0.55, 95 CI -0.70 to -0.40; 35 RCTs; 2366 participants; low-certainty evidence) and medium term (SMD -0.41 95% CI -0.79 to -0.03; 6 RCTs; 427 participants; low-certainty evidence). The SMD on the short term translates back to 6.8 fewer points on the MBI-EE. No studies reported the long-term effect. Focus on work-related, individual-level factors versus no intervention/no stress-reduction intervention Seven studies studied an intervention in which the focus is on altering work-related factors. The evidence is very uncertain about the short-term effects (no pooled effect estimate; three RCTs; 87 participants; very low-certainty evidence) and medium-term effects and long-term effects (no pooled effect estimate; two RCTs; 152 participants, and one RCT; 161 participants, very low-certainty evidence) of this type of stress management intervention.  A combination of individual-level interventions versus no intervention/wait list/no stress-reduction intervention Seventeen studies studied a combination of interventions. In the short-term, this type of intervention may result in a reduction in stress symptoms (SMD -0.67 95%, CI -0.95 to -0.39; 15 RCTs; 1003 participants; low-certainty evidence). The SMD translates back to 8.2 fewer points on the MBI-EE. On the medium term, a combination of individual-level interventions may result in a reduction in stress symptoms, but the evidence does not exclude no effect (SMD -0.48, 95% CI -0.95 to 0.00; 6 RCTs; 574 participants; low-certainty evidence). The evidence is very uncertain about the long term effects of a combination of interventions on stress symptoms (one RCT, 88 participants; very low-certainty evidence). Focus on stress versus other intervention type  Three studies compared focusing on stress versus focusing away from stress and one study a combination of interventions versus focusing on stress. The evidence is very uncertain about which type of intervention is better or if their effect is similar.

AUTHORS' CONCLUSIONS

Our review shows that there may be an effect on stress reduction in healthcare workers from individual-level stress interventions, whether they focus one's attention on or away from the experience of stress. This effect may last up to a year after the end of the intervention. A combination of interventions may be beneficial as well, at least in the short term. Long-term effects of individual-level stress management interventions remain unknown. The same applies for interventions on (individual-level) work-related risk factors. The bias assessment of the studies in this review showed the need for methodologically better-designed and executed studies, as nearly all studies suffered from poor reporting of the randomisation procedures, lack of blinding of participants and lack of trial registration. Better-designed trials with larger sample sizes are required to increase the certainty of the evidence. Last, there is a need for more studies on interventions which focus on work-related risk factors.

Assessment of Vigilance Level during Work: Fitting a Hidden Markov Model to Heart Rate Variability

This study aimed to enhance the real-time performance and accuracy of vigilance assessment by developing a hidden Markov model (HMM). Electrocardiogram (ECG) signals were collected and processed to remove noise and baseline drift. A group of 20 volunteers participated in the study. Their heart rate variability (HRV) was measured to train parameters of the modified hidden Markov model for a vigilance assessment. The data were collected to train the model using the Baum-Welch algorithm and to obtain the state transition probability matrix A^ and the observation probability matrix B^. Finally, the data of three volunteers with different transition patterns of mental state were selected randomly and the Viterbi algorithm was used to find the optimal state, which was compared with the actual state. The constructed vigilance assessment model had a high accuracy rate, and the accuracy rate of data prediction for these three volunteers exceeded 80%. Our approach can be used in wearable products to improve their vigilance level assessment functionality or in other fields that have key positions with high concentration requirements and monotonous repetitive work.

Wearable Multisensor Ring-Shaped Probe for Assessing Stress and Blood Oxygenation: Design and Preliminary Measurements

The increasing interest in innovative solutions for health and physiological monitoring has recently fostered the development of smaller biomedical devices. These devices are capable of recording an increasingly large number of biosignals simultaneously, while maximizing the user's comfort. In this study, we have designed and realized a novel wearable multisensor ring-shaped probe that enables synchronous, real-time acquisition of photoplethysmographic (PPG) and galvanic skin response (GSR) signals. The device integrates both the PPG and GSR sensors onto a single probe that can be easily placed on the finger, thereby minimizing the device footprint and overall size. The system enables the extraction of various physiological indices, including heart rate (HR) and its variability, oxygen saturation (SpO₂), and GSR levels, as well as their dynamic changes over time, to facilitate the detection of different physiological states, e.g., rest and stress. After a preliminary SpO₂ calibration procedure, measurements have been carried out in laboratory on healthy subjects to demonstrate the feasibility of using our system to detect rapid changes in HR, skin conductance, and SpO₂ across various physiological conditions (i.e., rest, sudden stress-like situation and breath holding). The early findings encourage the use of the device in daily-life conditions for real-time monitoring of different physiological states.

Continuous physiological signal measurement over 24-hour periods to assess the impact of work-related stress and workplace violence

Work-related stress has long been recognized as an essential factor affecting employees' health and wellbeing. Repeated exposure to acute occupational stressors puts workers at high risk for depression, obesity, hypertension, and early death. Assessment of the effects of acute stress on workers' wellbeing usually relies on subjective self-reports, questionnaires, or measuring biometric and biochemical markers in long-cycle time intervals. This study aimed to develop and validate the use of a multiparameter wearable armband for continuous non-invasive monitoring of physiological states. Two worker populations were monitored 24 h/day: six loggers for one day and six ICU nurses working 12-hr shifts for one week. Stress responses in nurses were highly correlated with changes in heart rate variability (HRV) and pulse transit time (PTT). A rise in the low-to high-frequency (LF/LH) ratio in HRV was also coincident with stress responses. HRV on workdays decreased compared to non-work days, and PTT also exhibited a persistent decrease reflecting increased blood pressure. Compared to loggers, nurses were involved in high-intensity work activities 45% more often but were less active on non-work days. The wearable technology was well accepted by all worker participants and yielded high signal quality, critical factors for long-term non-invasive occupational health monitoring.

Noninvasive Detection of Stress by Biochemical Profiles from the Skin

Stress is a leading cause of several disease types, yet it is underdiagnosed as current diagnostic methods are mainly based on self-reporting and interviews that are highly subjective, inaccurate, and unsuitable for monitoring. Although some physiological measurements exist (e.g., heart rate variability and cortisol), there are no reliable biological tests that quantify the amount of stress and monitor it in real time. In this article, we report a novel way to measure stress quickly, noninvasively, and accurately. The overall detection approach is based on measuring volatile organic compounds (VOCs) emitted from the skin in response to stress. Sprague Dawley male rats (n = 16) were exposed to underwater trauma. Sixteen naive rats served as a control group (n = 16). VOCs were measured before, during, and after induction of the traumatic event, by gas chromatography linked with mass spectrometry determination and quantification, and an artificially intelligent nanoarray for easy, inexpensive, and portable sensing of the VOCs. An elevated plus maze during and after the induction of stress was used to evaluate the stress response of the rats, and machine learning was used for the development and validation of a computational stress model at each time point. A logistic model classifier with stepwise selection yielded a 66-88% accuracy in detecting stress with a single VOC (2-hydroxy-2-methyl-propanoic acid), and an SVM (support vector machine) model showed a 66-72% accuracy in detecting stress with the artificially intelligent nanoarray. The current study highlights the potential of VOCs as a noninvasive, automatic, and real-time stress predictor for mental health.

An interpretable machine learning approach to multimodal stress detection in a simulated office environment

BACKGROUND AND OBJECTIVE

Work-related stress affects a large part of today's workforce and is known to have detrimental effects on physical and mental health. Continuous and unobtrusive stress detection may help prevent and reduce stress by providing personalised feedback and allowing for the development of just-in-time adaptive health interventions for stress management. Previous studies on stress detection in work environments have often struggled to adequately reflect real-world conditions in controlled laboratory experiments. To close this gap, in this paper, we present a machine learning methodology for stress detection based on multimodal data collected from unobtrusive sources in an experiment simulating a realistic group office environment (N=90).

METHODS

We derive mouse, keyboard and heart rate variability features to detect three levels of perceived stress, valence and arousal with support vector machines, random forests and gradient boosting models using 10-fold cross-validation. We interpret the contributions of features to the model predictions with SHapley Additive exPlanations (SHAP) value plots.

RESULTS

The gradient boosting models based on mouse and keyboard features obtained the highest average F1 scores of 0.625, 0.631 and 0.775 for the multiclass prediction of perceived stress, arousal and valence, respectively. Our results indicate that the combination of mouse and keyboard features may be better suited to detect stress in office environments than heart rate variability, despite physiological signal-based stress detection being more established in theory and research. The analysis of SHAP value plots shows that specific mouse movement and typing behaviours may characterise different levels of stress.

CONCLUSIONS

Our study fills different methodological gaps in the research on the automated detection of stress in office environments, such as approximating real-life conditions in a laboratory and combining physiological and behavioural data sources. Implications for field studies on personalised, interpretable ML-based systems for the real-time detection of stress in real office environments are also discussed.

A narrative review of the interconnection between pilot acute stress, startle, and surprise effects in the aviation context: Contribution of physiological measurements

Aviation remains one of the safest modes of transportation. However, an inappropriate response to an unexpected event can lead to flight incidents and accidents. Among several contributory factors, startle and surprise, which can lead to or exacerbate the pilot's state of stress, are often cited. Unlike stress, which has been the subject of much study in the context of driving and piloting, studies on startle and surprise are less numerous and these concepts are sometimes used interchangeably. Thus, the definitions of stress, startle, and surprise are reviewed, and related differences are put in evidence. Furthermore, it is proposed to distinguish these notions in the evaluation and to add physiological measures to subjective measures in their study. Indeed, Landman's theoretical model makes it possible to show the links between these concepts and studies using physiological parameters show that they would make it possible to disentangle the links between stress, startle and surprise in the context of aviation. Finally, we draw some perspectives to set up further studies focusing specifically on these concepts and their measurement.

Knowledge graph analysis and visualization of artificial intelligence applied in electrocardiogram

Background: Electrocardiogram (ECG) provides a straightforward and non-invasive approach for various applications, such as disease classification, biometric identification, emotion recognition, and so on. In recent years, artificial intelligence (AI) shows excellent performance and plays an increasingly important role in electrocardiogram research as well. Objective: This study mainly adopts the literature on the applications of artificial intelligence in electrocardiogram research to focus on the development process through bibliometric and visual knowledge graph methods. Methods: The 2,229 publications collected from the Web of Science Core Collection (WoSCC) database until 2021 are employed as the research objects, and a comprehensive metrology and visualization analysis based on CiteSpace (version 6.1. R3) and VOSviewer (version 1.6.18) platform, which were conducted to explore the co-authorship, co-occurrence and co-citation of countries/regions, institutions, authors, journals, categories, references and keywords regarding artificial intelligence applied in electrocardiogram. Results: In the recent 4 years, both the annual publications and citations of artificial intelligence in electrocardiogram sharply increased. China published the most articles while Singapore had the highest ACP (average citations per article). The most productive institution and authors were Ngee Ann Polytech from Singapore and Acharya U. Rajendra from the University of Technology Sydney. The journal Computers in Biology and Medicine published the most influential publications, and the subject with the most published articles are distributed in Engineering Electrical Electronic. The evolution of research hotspots was analyzed by co-citation references' cluster knowledge visualization domain map. In addition, deep learning, attention mechanism, data augmentation, and so on were the focuses of recent research through the co-occurrence of keywords.

A Multisensor System Embedded in a Computer Mouse for Occupational Stress Detection

Occupational stress is a major challenge in modern societies, related with many health and economic implications. Its automatic detection in an office environment can be a key factor toward effective management, especially in the post-COVID era of changing working norms. The aim of this study is the design, development and validation of a multisensor system embedded in a computer mouse for the detection of office work stress. An experiment is described where photoplethysmography (PPG) and galvanic skin response (GSR) signals of 32 subjects were obtained during the execution of stress-inducing tasks that sought to simulate the stressors present in a computer-based office environment. Kalman and moving average filters were used to process the signals and appropriately formulated algorithms were applied to extract the features of pulse rate and skin conductance. The results found that the stressful periods of the experiment significantly increased the participants' reported stress levels while negatively affecting their cognitive performance. Statistical analysis showed that, in most cases, there was a highly significant statistical difference in the physiological parameters measured during the different periods of the experiment, without and with the presence of stressors. These results indicate that the proposed device can be part of an unobtrusive system for monitoring and detecting the stress levels of office workers.

Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network

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.

Advances and challenges in the detection of academic stress and anxiety in the classroom: A literature review and recommendations

In recent years, stress and anxiety have been identified as two of the leading causes of academic underachievement and dropout. However, there is little work on the detection of stress and anxiety in academic settings and/or its impact on the performance of undergraduate students. Moreover, there is a gap in the literature in terms of identifying any computing, information technologies, or technological platforms that help educational institutions to identify students with mental health problems. This paper aims to systematically review the literature to identify the advances, limitations, challenges, and possible lines of research for detecting academic stress and anxiety in the classroom. Forty-four recent articles on the topic of detecting stress and anxiety in academic settings were analyzed. The results show that the main tools used for detecting anxiety and stress are psychological instruments such as self-questionnaires. The second most used method is acquiring and analyzing biological signals and biomarkers using commercial measurement instruments. Data analysis is mainly performed using descriptive statistical tools and pattern recognition techniques. Specifically, physiological signals are combined with classification algorithms. The results of this method for detecting anxiety and academic stress in students are encouraging. Using physiological signals reduces some of the limitations of psychological instruments, such as response time and self-report bias. Finally, the main challenge in the detection of academic anxiety and stress is to bring detection systems into the classroom. Doing so, requires the use of non-invasive sensors and wearable systems to reduce the intrinsic stress caused by instrumentation.

Occupational Stress Monitoring Using Biomarkers and Smartwatches: A Systematic Review

This article presents a systematic review of the literature concerning scientific publications on wrist wearables that can help to identify stress levels. The study is part of a research project aimed at modeling a stress surveillance system and providing coping recommendations. The investigation followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In total, 38 articles were selected for full reading, and 10 articles were selected owing to their alignment with the study proposal. The types of technologies used in the research stand out amongst our main results after analyzing the articles. It is noteworthy that stress assessments are still based on standardized questionnaires, completed by the participants. The main biomarkers collected by the devices used in the selected works included: heart rate variation, cortisol analysis, skin conductance, body temperature, and blood volume at the wrist. This study concludes that developing a wrist wearable for stress identification using physiological and chemical sensors is challenging but possible and applicable.

Real-time mental stress detection using multimodality expressions with a deep learning framework

Mental stress is becoming increasingly widespread and gradually severe in modern society, threatening people’s physical and mental health. To avoid the adverse effects of stress on people, it is imperative to detect stress in time. Many studies have demonstrated the effectiveness of using objective indicators to detect stress. Over the past few years, a growing number of researchers have been trying to use deep learning technology to detect stress. However, these works usually use single-modality for stress detection and rarely combine stress-related information from multimodality. In this paper, a real-time deep learning framework is proposed to fuse ECG, voice, and facial expressions for acute stress detection. The framework extracts the stress-related information of the corresponding input through ResNet50 and I3D with the temporal attention module (TAM), where TAM can highlight the distinguishing temporal representation for facial expressions about stress. The matrix eigenvector-based approach is then used to fuse the multimodality information about stress. To validate the effectiveness of the framework, a well-established psychological experiment, the Montreal imaging stress task (MIST), was applied in this work. We collected multimodality data from 20 participants during MIST. The results demonstrate that the framework can combine stress-related information from multimodality to achieve 85.1% accuracy in distinguishing acute stress. It can serve as a tool for computer-aided stress detection.

Event-related driver stress detection with smartphones among young novice drivers

Complex and diverse driving situations can pose short-term stressors to novice drivers. Continuously detecting stress is essential for driver training, stress intervention, and the design of in-vehicle information systems. This study designed and validated a driver stress detection method at the event level based on machine learning algorithms and facial features captured with smartphones. Thirty young novice drivers completed two driving tasks containing eight events of two versions (neutral and stressful), with psychological, physiological, and facial data collected. Four combinations of input data types and six machine learning algorithms were used to detect stressful events. The KNN algorithm with facial plus individual profile features yielded the highest accuracy of 89.2%. Adding individual profile features can improve classification performance. Facial areas such as brow, eye, jaw, nose, and mouth were most sensitive to stress. This approach could provide more temporal-spatial information about the driver's stress levels during the whole driving process. Practitioner Summary: This paper proposed a method to detect driver stress at the event level with smartphones. Models with facial plus individual profile features and the KNN algorithm had the most outstanding classification performance. The presented approach can serve as a tool for improving in-vehicle interaction system design when considering driver stress. Abbreviations: GSR: galvanic skin response; ECG: electrocardiography; HR: heart rate; HRV: heart rate variability; RGB: red green blue; NIR: near-infrared; IP: individual profile; DSI: driver stress inventory; APS: arousal predisposition scale; API: application programming interface; PPG: photoplethysmography; EDR: electrodermal response; PD: pupil diameter; SCL: skin conductance level; RF: random forest; KNN: k-nearest neighbour; LDA: linear discriminant analysis; QDA: quadratic discriminant analysis; SVML: support vector machines with the linear kernel; SVMP: support vector machines with the polynomial kernel; TP: true positive; TN: true negative; FP: false positive; FN: false negative; t-SNE: t-distributed stochastic neighbour embedding.

Comparing the Psychological Effects of Manikin-Based and Augmented Reality-Based Simulation Training: Within-Subjects Crossover Study

BACKGROUND

Patient simulators are an increasingly important part of medical training. They have been shown to be effective in teaching procedural skills, medical knowledge, and clinical decision-making. Recently, virtual and augmented reality simulators are being produced, but there is no research on whether these more realistic experiences cause problematic and greater stress responses as compared to standard manikin simulators.

OBJECTIVE

The purpose of this research is to examine the psychological and physiological effects of augmented reality (AR) in medical simulation training as compared to traditional manikin simulations.

METHODS

A within-subjects experimental design was used to assess the responses of medical students (N=89) as they completed simulated (using either manikin or AR) pediatric resuscitations. Baseline measures of psychological well-being, salivary cortisol, and galvanic skin response (GSR) were taken before the simulations began. Continuous GSR assessments throughout and after the simulations were captured along with follow-up measures of emotion and cortisol. Participants also wrote freely about their experience with each simulation, and narratives were coded for emotional word use.

RESULTS

Of the total 86 medical students who participated, 37 (43%) were male and 49 (57%) were female, with a mean age of 25.2 (SD 2.09, range 22-30) years and 24.7 (SD 2.08, range 23-36) years, respectively. GSR was higher in the manikin group adjusted for day, sex, and medications taken by the participants (AR-manikin: -0.11, 95% CI -0.18 to -0.03; P=.009). The difference in negative affect between simulation types was not statistically significant (AR-manikin: 0.41, 95% CI -0.72 to 1.53; P=.48). There was no statistically significant difference between simulation types in self-reported stress (AR-manikin: 0.53, 95% CI -2.35 to 3.42; P=.71) or simulation stress (AR-manikin: -2.17, 95% CI -6.94 to 2.59; P=.37). The difference in percentage of positive emotion words used to describe the experience was not statistically significant between simulation types, which were adjusted for day of experiment, sex of the participants, and total number of words used (AR-manikin: -4.0, 95% CI -0.91 to 0.10; P=.12). There was no statistically significant difference between simulation types in terms of the percentage of negative emotion words used to describe the experience (AR-manikin: -0.33, 95% CI -1.12 to 0.46; P=.41), simulation sickness (AR-manikin: 0.17, 95% CI -0.29 to 0.62; P=.47), or salivary cortisol (AR-manikin: 0.04, 95% CI -0.05 to 0.13; P=.41). Finally, preexisting levels of posttraumatic stress disorder, perceived stress, and reported depression were not tied to physiological responses to AR.

CONCLUSIONS

AR simulators elicited similar stress responses to currently used manikin-based simulators, and we did not find any evidence of AR simulators causing excessive stress to participants. Therefore, AR simulators are a promising tool to be used in medical training, which can provide more emotionally realistic scenarios without the risk of additional harm.

Speech Related Anxiety in Adults Who Stutter

Abstract. The relationship between anxiety and stuttering has always been a topic of debate with a great emphasis on research focused on examining whether speech-related anxiety can exacerbate stuttering. This investigation compares some speech-related anticipatory anxiety indices in fluent and dysfluent utterances in adults who stutter (AWS). We scored the level of cognitive speech-related anxiety (anticipatory anxiety) using a self-reporting method and also evaluated the autonomic aspects of anxiety (state anxiety) through recording changes in Galvanic Skin Response (GSR) signals. Explaining the link between stuttering and anxiety is expected to assist practitioners in stuttering assessment and subsequent treatment strategies. Phasic GSR values of six events related to answering the verbal stimuli through fluent and dysfluent responses were registered to measure sympathetic arousal as an index of state anxiety in 20 AWS ( Mage = 35 ± 4 years, range: 21–42). To quantitatively examine the cognitive aspects of speech-related anticipatory anxiety, two questionnaires were rated by participants addressing the stuttering anticipation and semantic difficulty of verbal stimuli. GSR measures of fluent events were significantly higher than dysfluent counterparts within time windows before and during answering aloud the verbal stimuli ( p < .001). Later in the experiment, GSR values of dysfluent events were found to be higher than their fluent counterparts ( p < .001). Stuttering anticipation yielded a weak negative meaningful correlation with the scores of fluency ( r = −0.283, p = .046) and a positive yet nonsignificant correlation with the stuttering scores. The semantic difficulty had a moderately significant correlation with stuttering anticipation ( r = 0.354, p = .012) but not a meaningful correlation with fluency state. Autonomic and cognitive indices of speech-related anticipatory anxiety are not robust predictors of fluency. Anxiety seems to be more of a consequence of stuttering than a cause.

The Advances of Immersive Virtual Reality Interventions for the Enhancement of Stress Management and Relaxation among Healthy Adults: A Systematic Review

The rapid changes in human contacts due to the COVID-19 crisis have not only posed a huge burden on the population’s health but may have also increased the demand for evidence-based psychological programs delivered through digital technology. A systematic review, following the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)” guidelines, was therefore conducted to explore the advances in stress management interventions utilizing VR and suggest up-to-date directions for future practice. The relevant literature was screened and the search resulted in 22,312 records, of which 16 studies were considered for analysis. The Methodological Index for Non-Randomized Studies (MINORS) was also employed to assess the quality of the included studies. The results suggest that VR-based interventions can facilitate positive changes in subjective stress levels and stress-related biomarkers. However, special attention should be paid to the development of rigorous VR protocols that embrace natural elements and concepts deriving from traditional treatment approaches, such as cognitive behavioral therapy techniques. Overall, this review aims to empower future researchers to grasp the opportunity that the COVID-19 pandemic generated and utilize digital technologies for strengthening individuals’ mental health. Future projects need to conduct large-scale VR studies to evaluate their effectiveness compared to other mental health interventions.

Using IoT devices for sensor-based monitoring of employees' mental workload: Investigating managers' expectations and concerns

Although the objective assessment of mental workload has been a focus of human factors research, few studies have investigated stakeholders' attitudes towards its implementation in real workplaces. The present study addresses this research gap by surveying N = 702 managers in three European countries (Germany, United Kingdom, Spain) about their expectations and concerns regarding sensor-based monitoring of employee mental workload. The data confirm the relevance of expectations regarding improvements of workplace design and employee well-being, as well as concerns about restrictions of employees' privacy and sovereignty, for the implementation of workload monitoring. Furthermore, Bayesian regression models show that the examined expectations have a substantial positive association with managers' willingness to support workload monitoring in their company. Privacy concerns are identified as a significant barrier to the acceptance of workload monitoring, both in terms of their prevalence among managers and their strong negative relationship with monitoring support.

Affective computing of multi-type urban public spaces to analyze emotional quality using ensemble learning-based classification of multi-sensor data

The quality of urban public spaces affects the emotional response of users; therefore, the emotional data of users can be used as indices to evaluate the quality of a space. Emotional response can be evaluated to effectively measure public space quality through affective computing and obtain evidence-based support for urban space renewal. We proposed a feasible evaluation method for multi-type urban public spaces based on multiple physiological signals and ensemble learning. We built binary, ternary, and quinary classification models based on participants’ physiological signals and self-reported emotional responses through experiments in eight public spaces of five types. Furthermore, we verified the effectiveness of the model by inputting data collected from two other public spaces. Three observations were made based on the results. First, the highest accuracies of the binary and ternary classification models were 92.59% and 91.07%, respectively. After external validation, the highest accuracies were 80.90% and 65.30%, respectively, which satisfied the preliminary requirements for evaluating the quality of actual urban spaces. However, the quinary classification model could not satisfy the preliminary requirements. Second, the average accuracy of ensemble learning was 7.59% higher than that of single classifiers. Third, reducing the number of physiological signal features and applying the synthetic minority oversampling technique to solve unbalanced data improved the evaluation ability.

A multimodal sensor dataset for continuous stress detection of nurses in a hospital

Advances in wearable technologies provide the opportunity to monitor many physiological variables continuously. Stress detection has gained increased attention in recent years, mainly because early stress detection can help individuals better manage health to minimize the negative impacts of long-term stress exposure. This paper provides a unique stress detection dataset created in a natural working environment in a hospital. This dataset is a collection of biometric data of nurses during the COVID-19 outbreak. Studying stress in a work environment is complex due to many social, cultural, and psychological factors in dealing with stressful conditions. Therefore, we captured both the physiological data and associated context pertaining to the stress events. We monitored specific physiological variables such as electrodermal activity, Heart Rate, and skin temperature of the nurse subjects. A periodic smartphone-administered survey also captured the contributing factors for the detected stress events. A database containing the signals, stress events, and survey responses is publicly available on Dryad.

Towards a Non-Contact Method for Identifying Stress Using Remote Photoplethysmography in Academic Environments

Stress has become a common condition and is one of the chief causes of university course disenrollment. Most of the studies and tests on academic stress have been conducted in research labs or controlled environments, but these tests can not be extended to a real academic environment due to their complexity. Academic stress presents different associated symptoms, anxiety being one of the most common. This study focuses on anxiety derived from academic activities. This study aims to validate the following hypothesis: by using a non-contact method based on the use of remote photoplethysmography (rPPG), it is possible to identify academic stress levels with an accuracy greater than or equal to that of previous works which used contact methods. rPPG signals from 56 first-year engineering undergraduate students were recorded during an experimental task. The results show that the rPPG signals combined with students' demographic data and psychological scales (the State-Trait Anxiety Inventory) improve the accuracy of different classification methods. Moreover, the results demonstrate that the proposed method provides 96% accuracy by using K-nearest neighbors, J48, and random forest classifiers. The performance metrics show better or equal accuracy compared to other contact methods. In general, this study demonstrates that it is possible to implement a low-cost method for identifying academic stress levels in educational environments.

Critical Problems for Research in Animal Sheltering, a Conceptual Analysis

Animal shelter research has seen significant increases in participation over the past several decades from academic organizations, private organizations, public entities, and even corporations that aims to improve shelter programs, processes, operations, and outcomes for the various stakeholders/participants involved in a shelter system (animals, humans, the community, wildlife, and the environment). These efforts are scattered through a huge variety of different research areas that are challenging to define and scope for organizations seeking to start new lines of research inquiry. This work aims to enumerate some of the most critical outstanding problems for research in animal sheltering in a conceptual framework that is intended to help direct research conversations toward the research topics of highest impact (with the highest quality outcomes possible). To this end, we define seven (7) key areas for research: animal behavior, adoptions and special needs populations, medical conditions, disease transmission, community, ecology, and wellness (one health), operations, and public-private-academic-corporate collaboration. Within each of these areas, we review specific problems and highlight examples of successes in each area in the past several decades. We close with a discussion of some of the topics that were not detailed in this manuscript but, nonetheless, deserve some mention. Through this enumeration, we hope to spur conversation around innovative methodologies, technologies, and concepts in both research and practice in animal sheltering.

An Empirical Approach to Analyzing the Effects of Stress on Individual Creativity in Business Problem-Solving: Emphasis on the Electrocardiogram, Electroencephalogram Methodology

In this study, experiments were conducted on 30 subjects by means of electrocardiogram (ECG) and electroencephalogram (EEG) methodologies as well as a money game to examine the effects of stress on creativity in business problem-solving. The study explained the relationship between creativity and human physiological response using the biopsychosocial model of challenge and threat. The subjects were asked to perform a cognitive mapping task. Based on the brain wave theory, we identified the types of brain waves and locations of brain activities that occurred during the creative problem-solving process in a business environment and studied the effects of stress on creativity. The results of the experiments showed significant differences in creativity in business problem-solving depending on whether or not stress was triggered. Differences were found in the time domain (SDNN, RMSSD) and frequency domain (HF, LF/HF ratio) of heart rates, a physiological stress indicator, between the stress group and the no-stress group. A brain wave analysis confirmed that alpha waves increased in the frontal lobe of the brain during creative business problem-solving but decreased when the subjects were under stress, during which beta waves in the brain increased. This study seeks to examine creativity in business problem-solving by studying the effects of stress on human physiological response and cognitive functions in the hope of providing a new and objective interpretation of existing research results.

Risk communication about work-related stress disorders in healthcare workers: a scoping review

Purposes

Healthcare workers are at risk of stress-related disorders. Risk communication can be an effective preventive health measure for some health risks, but is not yet common in the prevention of stress-related disorders in an occupational healthcare setting. The overall aim is to examine whether risk communication was part of interventions aimed at the prevention of stress-related disorders in healthcare workers.

Method

We performed a scoping review using the framework of Arksey and O’Malley. We searched in Medline, Web of Science and PsychInfo for studies reporting on preventive interventions of stress-related disorders in healthcare workers between 2005 and December 2020. Studies were included when the intervention reported on at least one element of risk communication and one goal. We predefined four elements of risk communication: risk perception, communication of early stress symptoms, risk factors and prevention; and three goals: inform, stimulate informed decision-making and motivate action.

Results

We included 23 studies that described 17 interventions. None of the included interventions were primarily developed as risk communication interventions, but all addressed the goals. Two interventions used all four elements of risk communication. The prominent mode of delivery was face to face, mostly delivered by researchers. Early stress symptoms and risk factors were measured by surveys.

Conclusions

Risk communication on risk factors and early signs of stress-related disorders is not that well studied and evaluated in an occupational healthcare setting. Overall, the content of the communication was not based on the risk perception of the healthcare workers, which limited the likelihood of them taking action.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00420-022-01851-x.

Validation of a Light EEG-Based Measure for Real-Time Stress Monitoring during Realistic Driving

Driver’s stress affects decision-making and the probability of risk occurrence, and it is therefore a key factor in road safety. This suggests the need for continuous stress monitoring. This work aims at validating a stress neurophysiological measure—a Neurometric—for out-of-the-lab use obtained from lightweight EEG relying on two wet sensors, in real-time, and without calibration. The Neurometric was tested during a multitasking experiment and validated with a realistic driving simulator. Twenty subjects participated in the experiment, and the resulting stress Neurometric was compared with the Random Forest (RF) model, calibrated by using EEG features and both intra-subject and cross-task approaches. The Neurometric was also compared with a measure based on skin conductance level (SCL), representing one of the physiological parameters investigated in the literature mostly correlated with stress variations. We found that during both multitasking and realistic driving experiments, the Neurometric was able to discriminate between low and high levels of stress with an average Area Under Curve (AUC) value higher than 0.9. Furthermore, the stress Neurometric showed higher AUC and stability than both the SCL measure and the RF calibrated with a cross-task approach. In conclusion, the Neurometric proposed in this work proved to be suitable for out-of-the-lab monitoring of stress levels.

Assessment of the Psychophysiological State of Female Operators Under Simulated Microgravity

The article describes methods of non-verbal speech characteristics analysis used to determine psychophysiological state of female subjects under simulated microgravity conditions ("dry" immersion, DI), as well as the results of the study. A number of indicators of the acute period of adaptation to microgravity conditions was described. The acute adaptation period in female subjects began earlier (evening of the 1st day of DI) and ended faster than in male ones in previous studies (2nd day of DI). This was indicated by a decrease in the level of state anxiety (STAI, p < 0,05) and depression-dejection [Profile of Mood States (POMS), p < 0,05], as well as a decrease in pitch (p < 0,05) and voice intensity (p < 0,05). In addition, women, apparently, used the "freeze" coping strategy - the proportion of neutral facial expressions on the most intense days of the experiment was at maximum. The subjects in this experiment assessed their feelings and emotions better, giving more accurate answers in self-assessment questionnaires, but at the same time tried to look and sound as calm and confident as possible, controlling their expressions. Same trends in the subjects' cognitive performance were identified as in similar experimental conditions earlier: the subjects' psychophysiological excitement corresponded to better performance in sensorimotor tasks. The difference was in the speed of mathematical computation: women in the present study performed the computation faster on the same days when they made fewer pauses in speech, while in men in previous experiments this relationship was inverse.

Work environment risk factors causing day-to-day stress in occupational settings: a systematic review

Background

While chronic workplace stress is known to be associated with health-related outcomes like mental and cardiovascular diseases, research about day-to-day occupational stress is limited. This systematic review includes studies assessing stress exposures as work environment risk factors and stress outcomes, measured via self-perceived questionnaires and physiological stress detection. These measures needed to be assessed repeatedly or continuously via Ecological Momentary Assessment (EMA) or similar methods carried out in real-world work environments, to be included in this review. The objective was to identify work environment risk factors causing day-to-day stress.

Methods

The search strategies were applied in seven databases resulting in 11833 records after deduplication, of which 41 studies were included in a qualitative synthesis. Associations were evaluated by correlational analyses.

Results

The most commonly measured work environment risk factor was work intensity, while stress was most often framed as an affective response. Measures from these two dimensions were also most frequently correlated with each other and most of their correlation coefficients were statistically significant, making work intensity a major risk factor for day-to-day workplace stress.

Conclusions

This review reveals a diversity in methodological approaches in data collection and data analysis. More studies combining self-perceived stress exposures and outcomes with physiological measures are warranted.

Sources of Occupational Stress among Office Workers—A Focus Group Study

Workplace stress remains a major interest of occupational health research, usually based on theoretical models and quantitative large-scale studies. Office workers are especially exposed to stressors such as high workload and time pressure. The aim of this qualitative research was to follow a phenomenological approach to identify work stressors as they are perceived by office workers. Six focus groups with office workers of different occupations were conducted in Belgium and Slovenia. A total of 39 participants were included in the study. We used the RQDA software for data processing and analysis and the seven job-quality indices of the European Working Conditions Survey (EWCS) to structure our findings. The results show that work intensity and social environment proved to be main stress categories, followed by skills and discretion, prospects, and working time quality. The physical environment and earnings were not covered in our results. We created organisational (structural/process-oriented and financial) stressors and office workers’ physical health as two additional categories since these topics did not fit into the EWCS. While our findings mainly confirm data from existing occupational stress literature and emphasise the multi-level complexity of work stress experiences, this paper suggests that there are relevant stressors experienced by office workers beyond existing quantitative frameworks.