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Work-related stress in industrial environments presents significant challenges, affecting both employee well-being and organizational productivity. This study investigates the use of wearable devices for stress detection in controlled laboratory settings, focusing on the variability of stress induction protocols and physiological monitoring devices, such as the Empatica E4 and Zephyr BioHarness 3. Key physiological indicators, including heart rate variability and electrodermal activity, were analyzed, and a support vector machine algorithm was employed to classify stress levels with high accuracy. The primary contribution of this research lies in evaluating the adaptability of machine learning models across multiple stress protocols and device types, providing insights into their effectiveness in different controlled environments. These findings highlight the ongoing challenge of translating laboratory results to real-world industrial settings, where task complexity and environmental variability pose additional difficulties. [ABSTRACT FROM AUTHOR]

When loaded with difficulties in fulfilling daily requirements, a lot of people in today's world experience an emotional pressure known as stress. Stress that lasts for a short duration of time has more advantages as they are good for mental health. But, the persistence of stress for a long duration of time may lead to serious health impacts in individuals, such as high blood pressure, cardiovascular disease, stroke and so on. Long-term stress, if unidentified and not treated, may also result in personality disorder, depression and anxiety. The initial detection of stress has become more important to prevent the health issues that arise due to stress. Detection of stress based on brain signals for analysing the emotion in humans leads to accurate detection outcomes. Using EEG-based detection systems and disease, disability and disorders can be identified from the brain by utilising the brain waves. Sentiment Analysis (SA) is helpful in identifying the emotions and mental stress in the human brain. So, a system to accurately and precisely detect depression in human based on their emotion through the utilisation of SA is of high necessity. The development of a reliable and precise Emotion and Stress Recognition (ESR) system in order to detect depression in real-time using deep learning techniques with the aid of Electroencephalography (EEG) signal-based SA is carried out in this paper. The essentials needed for performing stress and emotion detection are gathered initially from benchmark databases. Next, the pre-processing procedures, like the removal of artifacts from the gathered EEG signal, are carried out on the implemented model. The extraction of the spectral attributes is carried out from the pre- processed. The extracted spectral features are considered the first set of features. Then, with the aid of a Conditional Variational Autoencoder (CVA), the deep features are extracted from the pre-processed signals forming a second set of features. The weights are optimised using the Adaptive Egret Swarm Optimisation Algorithm (AESOA) so that the weighted fused features are obtained from these two sets of extracted features. Then, a Cascaded Deep Temporal Convolution Network with Attention Mechanism (CDTCN-AM) is used to recognise stress and emotion. The validation of the results from the developed stress and emotion recognition approach is carried out against traditional models in order to showcase the effectiveness of the suggested approach. [ABSTRACT FROM AUTHOR]

Increasingly, humans are exposed to different activities at work, at home, and in general in their daily lives that generate episodes of stress. In many cases, these episodes could produce disorders in their health and reduce their quality of life. For this reason, it is crucial to implement mechanisms that can detect stress in individuals and develop applications that provide feedback through various activities to help reduce stress levels. Physiological parameters, such as galvanic skin response (GSR) and heart rate (HR) are indicative of stress-related changes. There exist methodologies that use wearable sensors to measure these stress levels. In this study, a sensor of blood volume pulse (BVP) and an electrocardiography (ECG) sensor were utilized to obtain metrics like heart rate variability (HRV) and pulse arrival time (PAT). Their features were extracted, processed, and analyzed for anxiety detection. The classification performance was evaluated using decision trees, a support vector machine (SVM), and meta-classifiers to accurately distinguish between "stressed" and "non-stressed" states. We obtained the best results with the SVM classifier using all the features. Additionally, we found that the ECG AD8232 sensor provided more reliable data compared to the photoplethysmography (PPG) signal obtained from the MAX30100 sensor. Therefore, the ECG is a more accurate tool for assessing emotional states related to stress and anxiety. [ABSTRACT FROM AUTHOR]

In the domain of education, the integration of machine learning methodologies for student assessment and stress detection has emerged as a critical area of investigation. This paper explores the application of computationally intelligent techniques, particularly sentiment analysis and natural language processing, for the analysis and interpretation of teachers' textual feedback in academic reports. Through sentiment analysis, qualitative feedback is quantified, enabling a comprehensive evaluation of students' academic progress encompassing behavioural aspects, attendance, and achievement. Moreover, this study investigates the correlation between learning behaviour data and classroom performance, emphasising the importance of analysing unstructured data including video, audio, and image inputs to comprehend students' behaviours and learning patterns. Utilising big data technology and predictive modelling, this research aims to augment teaching efficacy, enhance learning outcomes, and establish a framework for real-time assessment of teachers' performance. Furthermore, the project endeavours to develop a system capable of discerning students' emotions and stress levels using machine learning algorithms. By analysing facial expressions and other behavioural indicators, the system endeavours to furnish timely feedback to both teachers and students, fostering a supportive learning milieu conducive to academic achievement. In essence, this research underscores the significance of harnessing advanced technologies such as machine learning, sentiment analysis, and predictive modelling to enrich student assessment, stress detection, and overall educational achievements. [ABSTRACT FROM AUTHOR]

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Stress recognition, particularly using machine learning (ML) with physiological data such as heart rate variability (HRV), holds promise for mental health interventions. However, limited datasets in affective computing and healthcare research can lead to inaccurate conclusions regarding the ML model performance. This study employed supervised learning algorithms to classify stress and relaxation states using HRV measures. To account for limitations associated with small datasets, robust strategies were implemented based on methodological recommendations for ML with a limited dataset, including data segmentation, feature selection, and model evaluation. Our findings highlight that the random forest model achieved the best performance in distinguishing stress from non-stress states. Notably, it showed higher performance in identifying stress from relaxation (F1-score: 86.3%) compared to neutral states (F1-score: 65.8%). Additionally, the model demonstrated generalizability when tested on independent secondary datasets, showcasing its ability to distinguish between stress and relaxation states. While our performance metrics might be lower than some previous studies, this likely reflects our focus on robust methodologies to enhance the generalizability and interpretability of ML models, which are crucial for real-world applications with limited datasets. [ABSTRACT FROM AUTHOR]

Smartwatch health sensor data are increasingly utilized in smart health applications and patient monitoring, including stress detection. However, such medical data often comprise sensitive personal information and are resource-intensive to acquire for research purposes. In response to this challenge, we introduce the privacy-aware synthetization of multi-sensor smartwatch health readings related to moments of stress, employing Generative Adversarial Networks (GANs) and Differential Privacy (DP) safeguards. Our method not only protects patient information but also enhances data availability for research. To ensure its usefulness, we test synthetic data from multiple GANs and employ different data enhancement strategies on an actual stress detection task. Our GAN-based augmentation methods demonstrate significant improvements in model performance, with private DP training scenarios observing an 11.90–15.48% increase in F1-score, while non-private training scenarios still see a 0.45% boost. These results underline the potential of differentially private synthetic data in optimizing utility–privacy trade-offs, especially with the limited availability of real training samples. Through rigorous quality assessments, we confirm the integrity and plausibility of our synthetic data, which, however, are significantly impacted when increasing privacy requirements. [ABSTRACT FROM AUTHOR]

Stress has been identified as one of major causes of health issues. To detect the stress levels with higher accuracy, fusion of multimodal physiological signals is a promising technique. However, there is an asynchrony between physiological signals observed from different perspectives. Exploring the temporal alignment relationship between modalities is helpful to improve the quality of multimodal fusion. This paper proposes an end-to-end multimodal stress detection model based on Bidirectional Cross- and Self-modal Attention (BCSA) mechanism. Specifically, we first construct different feature extractors based on the characteristics of Blood Volume Pulse (BVP) and Electrodermal Activity (EDA) to complete automated temporal feature extraction. Second, cross-modal attention is used to seek the alignment relationship between the two modalities and fully fuse cross-modal information. The self-modal attention is used to attenuate noise and redundant information, highlight important information and obtain salient stress representations. Finally, the stress representations of the two modalities are processed separately, and the mean square error (MSE) is used to narrow the gap between them. Experimental results on the UBFC-Phys dataset and WESAD dataset show that the proposed model can effectively improve the accuracy of stress recognition, and outperforms several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The need for stress recognition research is increasing with the increase in the need for proactively managing stress, which greatly impacts overall health. If stress levels can be measured through these stress perception studies, it is thought that the repulsion that stress measurement equipment can give can be minimized, and efficient management can be performed as an auxiliary means of disease management through stress. Although many studies are being conducted on stress recognition using physiological signals, the equipment required to acquire these signals generates additional costs and is inconvenient for the wearer when worn continuously. By contrast, studies using facial images for recognizing stress use noncontact methods. However, these methods have a disadvantage, that is, if a subject does not demonstrate major changes in their facial expressions, recognizing their stress state is difficult. In this study, we propose a stress-recognition method using both facial images and speech to overcome the aforementioned problems. By using speech signals, the problems that occur when using facial images only can be overcome. In the proposed method, the modality models of image and speech are optimized to be suitable for stress recognition. Then, in the network of each modality model, the feature maps of the middle layer are combined with a multimodal neglecting mask module. The two models are efficiently combined by learning the parts to be neglected. The proposed method used the multimodal neglecting mask module (MNMM) to extract features that are more relevant to stress recognition compared to previous methods. The experimental results confirm that the proposed method exhibits the highest performance of 78.1246%, among those exhibited by other reported methods when classifying the stress states into three classes, namely, neutral, low stress, and high stress.

Increasingly, humans are exposed to different activities at work, at home, and in general in their daily lives that generate episodes of stress. In many cases, these episodes could produce disorders in their health and reduce their quality of life. For this reason, it is crucial to implement mechanisms that can detect stress in individuals and develop applications that provide feedback through various activities to help reduce stress levels. Physiological parameters, such as galvanic skin response (GSR) and heart rate (HR) are indicative of stress-related changes. There exist methodologies that use wearable sensors to measure these stress levels. In this study, a sensor of blood volume pulse (BVP) and an electrocardiography (ECG) sensor were utilized to obtain metrics like heart rate variability (HRV) and pulse arrival time (PAT). Their features were extracted, processed, and analyzed for anxiety detection. The classification performance was evaluated using decision trees, a support vector machine (SVM), and meta-classifiers to accurately distinguish between “stressed” and “non-stressed” states. We obtained the best results with the SVM classifier using all the features. Additionally, we found that the ECG AD8232 sensor provided more reliable data compared to the photoplethysmography (PPG) signal obtained from the MAX30100 sensor. Therefore, the ECG is a more accurate tool for assessing emotional states related to stress and anxiety.

The widespread adoption of the smartphone has led to both positive and negative consequences for adolescents' mental health. This study examines the interplay between smartphone dependence (SPD), generalized anxiety disorder (GAD), and various mental health outcomes among Korean adolescents. Data from the 16th Adolescence Health Behavior Survey (2020), including 54,948 middle and high school students, were analyzed. Adolescents were categorized into three groups based on SPD severity. The GAD-7 scale assessed anxiety, and other factors such as subjective health recognition, happiness, weight control efforts, and body mass index (BMI) were considered. Adolescents with higher SPD exhibited lower academic performance, decreased happiness, and increased perception of stress. GAD levels were positively correlated with SPD, with higher SPD linked to more severe GAD symptoms. Additionally, higher SPD was associated with increased loneliness, sadness, and suicidal thoughts, plans, and attempts as well as a greater likelihood of habitual drug use. Gender differences revealed that females were more prone to sadness, hopelessness, and suicidal thoughts, while males exhibited higher rates of drug use. This study highlights the complex relationship between SPD, GAD, and mental health outcomes among Korean adolescents. Stress recognition was found to mediate the association between GAD and SPD. The process-macro result of the total effect between SPD on GAD and the direct effect of the SPD pathway on GAD was significant; thus, the stress recognition was mediated. Effective interventions should target stress management, especially among adolescents with high smartphone dependence, to mitigate the risk of mental health issues. These findings underscore the importance of addressing smartphone dependence and its impact on the mental well-being of adolescents. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) signals offer invaluable insights into diverse activities of the human brain, including the intricate physiological and psychological responses associated with mental stress. A major challenge, however, is accurately identifying mental stress while mitigating the limitations associated with a large number of EEG channels. Such limitations encompass computational complexity, potential overfitting, and the prolonged setup time for electrode placement, all of which can hinder practical applications. To address these challenges, this study presents the novel CCHP method, aimed at identifying and ranking commonly optimal EEG channels based on their sensitivity to the mental stress state. This method's uniqueness lies in its ability not only to find common channels, but also to prioritize them according to their responsiveness to stress, ensuring consistency across subjects and making it potentially transformative for real-world applications. From our rigorous examinations, eight channels emerged as universally optimal in detecting stress variances across participants. Leveraging features from the time, frequency, and time–frequency domains of these channels, and employing machine learning algorithms, notably RLDA, SVM, and KNN, our approach achieved a remarkable accuracy of 81.56% with the SVM algorithm outperforming existing methodologies. The implications of this research are profound, offering a stepping stone toward the development of real-time stress detection devices, and consequently, enabling clinicians to make more informed therapeutic decisions based on comprehensive brain activity monitoring. [ABSTRACT FROM AUTHOR]

This paper introduces a study on stress recognition utilizing mobile EEG and GSR sensors. The research involved collecting samples from a group of 55 refugees who participated in Virtual Reality stress-reduction sessions. The timing of the study coincided with an influx of refugees, prompting the development of software specifically designed to alleviate acute stress among them. The paper focuses on presenting an EEG/GSR signals pipeline for classifying stress levels, emphasizing selecting the most informative features. The classification process employed popular machine learning methods, yielding results of 86.7% for two-stress-level classification and 82.3% and 67.7% for the three- and five-level classifications, respectively. Most importantly, the positive impact of the system has been proven by subjective assessment in alignment with objective features analysis. Such a system has not yet reached the level of autonomy, but it can be a valuable support tool for mental health professionals. [ABSTRACT FROM AUTHOR]

The factors that influence a person's mental health are numerous, interconnected, and multi‐dimensional. Recognition of stress is one of the facets in developing the Mental Healthcare (MHC) system framework. With the advent of technology, smart wearable devices have paved a way to collect data in real‐time to provide the cutting‐edge reports about the individual. Due to the physiological sensors present in the smart wearable devices, it is now possible to have a robust system to recognize the stress of the smart wearable devices user thus consecutively leading to recognition of factors in leading to stress. However, the current MHC system for recognition and identification of stress have several drawbacks. First, stress recognition is mostly designed for a particular group of individuals like occupational stress, perinatal maternal stress, or health worker stress and fails to propose a framework that would not be targeted for a particular group of individual. Second, most of the previous work done on stress recognition focuses on the extraction of handcrafted features thus requiring human intervention and expertise. To address these issues, this study proposes, a hybrid deep learning based ensemble approach for automated extraction of features and classification into various state of stress for MHC system. The proposed framework takes input from wearable physiological sensors and is provided to deep learning classifier of convolutional neural network (CNN) and CNN‐long short term memory based ensemble model. The proposed framework has been experimented on the wearable stress and affect detection dataset and reports an accuracy of 91.52% that is 7.20% higher than earlier reported accuracies from other machine learning and deep learning models. [ABSTRACT FROM AUTHOR]

One of the many areas in which artificial intelligence (AI) techniques are used is the development of systems for the recognition of vital emotions to control human health and safety. This study used biometric sensors in a multimodal approach to capture signals in the recognition of stressful situations. The great advances in technology have allowed the development of portable devices capable of monitoring different physiological measures in an inexpensive, non-invasive, and efficient manner. Virtual reality (VR) has evolved to achieve a realistic immersive experience in different contexts. The combination of AI, signal acquisition devices, and VR makes it possible to generate useful knowledge even in challenging situations in daily life, such as when driving. The main goal of this work is to combine the use of sensors and the possibilities offered by VR for the creation of a system for recognizing stress during different driving situations in a vehicle. We investigated the feasibility of detecting stress in individuals using physiological signals collected using a photoplethysmography (PPG) sensor incorporated into a commonly used wristwatch. We developed an immersive environment based on VR to simulate experimental situations and collect information on the user's reactions through the detection of physiological signals. Data collected through sensors in the VR simulations are taken as input to several models previously trained by machine learning (ML) algorithms to obtain a system that performs driver stress detection and high-precision classification in real time. [ABSTRACT FROM AUTHOR]

In this study, we aimed to explore whether eating alone is associated with mental health conditions in Korean adolescents. The data of 2,012 Korean adolescents aged 12-18 years were obtained from the Korea National Health and Nutrition Examination Survey 2015–2019. Participants were classified into three groups based on the frequency of eating alone: none (all meals with others); 1 meal/day alone; and ≥2 meals/day alone. Mental health conditions were assessed based on stress recognition, depressive symptoms, and suicidal ideation. Multivariable logistic regressions were employed to calculate the adjusted odds ratios (AORs) and 95% confidence intervals (CIs) of poor mental health conditions according to the frequency of eating alone. Adolescents who ate ≥2 meals/day alone had higher odds of stress recognition (AOR: 2.65, 95% CI: 1.94- 3.63), depressive symptoms (AOR: 2.55, 95% CI: 1.47-4.42), and suicidal ideation (AOR: 2.53, 95% CI: 1.05-6.08) than those who ate all their meals with others. In addition, having breakfast or dinner alone increased the odds of stress recognition. Considering the continuous increase in the social phenomenon of eating alone, nutritional educations are needed to develop adolescents' ability to choose more nutritionally balanced and healthy meals when eating alone. [ABSTRACT FROM AUTHOR]

Objective: This study aims to determine the factors affecting the perception levels of operating room (OR) nurses and nurse anesthetists working in the OR regarding patient safety attitudes. Materials and Methods: This study was conducted using face-to-face interviews with 117 healthcare professionals working as OR nurses (n=60) and nurse anesthetists (n=57). The patient safety attitude questionnaire (SAQ), where the reliability analysis was also performed for the SAQ scale. and sociodemographic characteristics were used for this study. Qualitative variables were given as numbers and percentages (%), and the dataset belonging to quantitative variables that met the normal distribution criteria was given as mean (standard deviation), and data of quantitative variables that did not comply with normality were given as median, IQR, and 95% CI of the median. Results: There were significant differences between OR nurses and nurse anesthetists regarding job satisfaction (p=0.015) and total SAQ score (p=0.040). Significant differences were detected between men and women participants regarding smoking (p=0.020) and stress recognition (p=0.040). The reliability analysis of the scale was as follows: total (α: 0.791), job satisfaction (α: 0.883), teamwork climate (α: 0.856), safety climate (α: 0.864), perceptions of management (α: 0.881), stress recognition (α: 0.791), and working conditions (α: 0.530). Conclusion: It was shown that the patient safety attitudes of the healthcare professionals participating in this study are above average, although it is still insufficient, where the stress identification score of the female participant was higher, and it was also found that the nurses' job satisfaction and SAQ score were higher. [ABSTRACT FROM AUTHOR]

As humans, we experience social stress in countless everyday-life situations. Giving a speech in front of an audience, passing a job interview, and similar experiences all lead us to go through stress states that impact both our psychological and physiological states. Therefore, studying the link between stress and physiological responses had become a critical societal issue, and recently, research in this field has grown in popularity. However, publicly available datasets have limitations. In this article, we propose a new dataset, UBFC-Phys, collected with and without contact from participants living social stress situations. A wristband was used to measure contact blood volume pulse (BVP) and electrodermal activity (EDA) signals. Video recordings allowed to compute remote pulse signals, using remote photoplethysmography (RPPG), and facial expression features. Pulse rate variability (PRV) was extracted from BVP and RPPG signals. Our dataset permits to evaluate the possibility of using video-based physiological measures compared to more conventional contact-based modalities. The goal of this article is to present both the dataset, which we make publicly available, and experimental results of contact and non-contact data comparison, as well as stress recognition. We obtained a stress state recognition accuracy of 85.48 percent, achieved by remote PRV features. [ABSTRACT FROM AUTHOR]

Acute stress is a physiological condition that may induce several neural dysfunctions with a significant impact on life quality. Accordingly, it would be important to monitor stress in everyday life unobtrusively and inexpensively. In this paper, we presented a new methodological pipeline to recognize acute stress conditions using electrodermal activity (EDA) exclusively. Particularly, we combined a rigorous and robust model (cvxEDA) for EDA processing and decomposition, with an algorithm based on a support vector machine to classify the stress state at a single-subject level. Indeed, our method, based on a single sensor, is robust to noise, applies a rigorous phasic decomposition, and implements an unbiased multiclass classification. To this end, we analyzed the EDA of 65 volunteers subjected to different acute stress stimuli induced by a modified version of the Trier Social Stress Test. Our results show that stress is successfully detected with an average accuracy of 94.62 percent. Besides, we proposed a further 4-class pattern recognition system able to distinguish between non-stress condition and three different stressful stimuli achieving an average accuracy as high as 75.00 percent. These results, obtained under controlled conditions, are the first step towards applications in ecological scenarios. [ABSTRACT FROM AUTHOR]

The brain is more sensitive to stress than other organs and can develop many diseases under excessive stress. In this study, we developed a method to improve the accuracy of emotional stress recognition using multi-channel electroencephalogram (EEG) signals. The method combines a three-dimensional (3D) convolutional neural network with an attention mechanism to build a 3D convolutional gated self-attention neural network. Initially, the EEG signal is decomposed into four frequency bands, and a 3D convolutional block is applied to each frequency band to obtain EEG spatiotemporal information. Subsequently, long-range dependencies and global information are learned by capturing prominent information from each frequency band via a gated self-attention mechanism block. Using frequency band mapping, complementary features are learned by connecting vectors from different frequency bands, which is reflected in the final attentional representation for stress recognition. Experiments conducted on three benchmark datasets for assessing the performance of emotional stress recognition indicate that the proposed method outperforms other conventional methods. The performance analysis of proposed methods confirms that EEG pattern analysis can be used for studying human brain activity and can accurately distinguish the state of stress. [ABSTRACT FROM AUTHOR]

The nonverbal communication processes a critical parcel. In some cases verbal communication is incapable since the speaker does not utilize non-verbal communication well at the same time. Non-verbal communication which falls in unconscious emotion is important in determining in function of cognition, language comprehension, and decision making. However, a little research studied in this area. Many years, researchers are amazed by the reliability of Mel-Frequency Cepstral Coefficients (MFCC) feature extraction technique in recognizing stressed speech. In this paper, we propose a simple feature extraction technique that effective but strong enough to recognize stressed speech. There are the speech energy and frequency. We attempted a basic approach to classify unbiased or stretch on female and male discourse. The highlight extraction is based on control and recurrence. This investigate utilized 10 female and 10 male discourse datasets. There are 5 classification strategies utilized. The classification models are Neural Arrange, k-Nearest Neighbour, Bolster Vector Machine, combination of NN-k-NN and combination of NN-SVM. Test comes about approved utilizing k-fold cross-validation strategy. The tests are assessed utilizing R-index to distinguish whether the highlights contributing to the push discourse acknowledgment. Based on exploratory comes about, the number of inputs impacts the esteem of R-index. In general, combining the Neural Organize and Back Vector Machine is the most excellent classification strategy by appearing stretch acknowledgment rate of 85% precision. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The effective detection and quantification of mental health has always been an important research topic. Heart rate variability (HRV) analysis is a useful tool for detecting psychological stress levels. However, there is no consensus on the optimal HRV metrics in psychological assessments. This study proposes an HRV analysis method that is based on heartbeat modes to detect drivers' stress. We used statistical tools for linguistics to detect and quantify the structure of the heart rate time series and summarized different heartbeat modes in the time series. Based on the k-nearest neighbors (k-NN) classification algorithm, the probability of each heartbeat mode was used as a feature to detect and recognize stress caused by the driving environment. The results indicated that the stress from the driving environment changed the heartbeat mode. Stress-related heartbeat modes were determined, facilitating detection of the stress state with an accuracy of 93.7%. We also concluded that the heartbeat mode was correlated to the galvanic skin response (GSR) signal, reflecting real-time abnormal mood fluctuations. The proposed method revealed HRV characteristics that made quantifying and detecting different mental conditions possible. Thus, it would be feasible to achieve personalized analyses to further study the interaction between physiology and psychology. [ABSTRACT FROM AUTHOR]

Stress is a normal reaction of the human organism which triggered in situations that require a certain level of activation. This reaction has both positive and negative effects on everyone's life. Therefore, stress management is of vital importance in maintaining the psychological balance of a person. Thermal-based imaging technique is becoming popular among researchers due to its non-contact conductive nature. Moreover, thermal-based imaging has shown promising results in detecting stress in a non-contact and non-invasive manner. Compared to other non-contact stress detection methods such as pupil dilation, keystroke behavior, social media interaction and voice modulation, thermal-based imaging provides better features with clear boundaries and requires no heavy methodology. This paper presented a brief review of previous work on thermal imaging related stress detection in humans. This paper also presented the stages of stress detection based on thermal face signatures such as dataset type, thermal image face detection, feature descriptors and classification performance comparisons are presented. This paper can help future researchers to understand stress detection based on thermal imaging by presenting the popular methods previous researchers use for stress detection based on thermal images. [ABSTRACT FROM AUTHOR]

Researchers strive hard to develop effective ways to detect and cope with enduring high-level daily stress as early as possible to prevent serious health consequences. Although research has traditionally been conducted in laboratory settings, a set of new studies have recently begun to be conducted in ecological environments with unobtrusive wearable devices. Since patterns of stress are ideographic, person-independent models have generally lower accuracies. On the contrary, person-specific models have higher accuracies but they require a long-term data collection period. In this study, we developed a hybrid approach of personal level stress clustering by using baseline stress self-reports to increase the success of person-independent models without requiring a substantial amount of personal data. We further added decision level smoothing to our unobtrusive smartwatch based stress level differentiation system to increase the performance by correcting false labels assigned by the machine learning algorithm. In order to test and evaluate our system, we collected physiological data from 32 participants of a summer school with wrist-worn unobtrusive wearable devices. This event is comprised of baseline, lecture, exam and recovery sessions. In the recovery session, a stress management method was applied to alleviate the stress of the participants. The perceived stress in the form of NASA-TLX questionnaires collected from the users as self-reports and physiological stress levels extracted using wearable sensors are examined separately. By using our system, we were able to differentiate the 3-levels of stress successfully. We further substantially increase our performance by personal stress level clustering and by applying high-level accuracy calculation and decision level smoothing methods. We also demonstrated the success of the stress reduction methods by analyzing physiological signals and self-reports.

The brain is more sensitive to stress than other organs and can develop many diseases under excessive stress. In this study, we developed a method to improve the accuracy of emotional stress recognition using multi-channel electroencephalogram (EEG) signals. The method combines a three-dimensional (3D) convolutional neural network with an attention mechanism to build a 3D convolutional gated self-attention neural network. Initially, the EEG signal is decomposed into four frequency bands, and a 3D convolutional block is applied to each frequency band to obtain EEG spatiotemporal information. Subsequently, long-range dependencies and global information are learned by capturing prominent information from each frequency band via a gated self-attention mechanism block. Using frequency band mapping, complementary features are learned by connecting vectors from different frequency bands, which is reflected in the final attentional representation for stress recognition. Experiments conducted on three benchmark datasets for assessing the performance of emotional stress recognition indicate that the proposed method outperforms other conventional methods. The performance analysis of proposed methods confirms that EEG pattern analysis can be used for studying human brain activity and can accurately distinguish the state of stress.

In the field of stress recognition, the majority of research has conducted experiments on datasets collected from controlled environments with limited stressors. As these datasets cannot represent real-world scenarios, stress identification and analysis are difficult. There is a dire need for reliable, large datasets that are specifically acquired for stress emotion with varying degrees of expression for this task. In this paper, we introduced a dataset for Stress Analysis with Dimensions of Valence and Arousal of Korean Movie in Wild (SADVAW), which includes video clips with diversity in facial expressions from different Korean movies. The SADVAW dataset contains continuous dimensions of valence and arousal. We presented a detailed statistical analysis of the dataset. We also analyzed the correlation between stress and continuous dimensions. Moreover, using the SADVAW dataset, we trained a deep learning-based model for stress recognition. [ABSTRACT FROM AUTHOR]

The effective detection and quantification of mental health has always been an important research topic. Heart rate variability (HRV) analysis is a useful tool for detecting psychological stress levels. However, there is no consensus on the optimal HRV metrics in psychological assessments. This study proposes an HRV analysis method that is based on heartbeat modes to detect drivers’ stress. We used statistical tools for linguistics to detect and quantify the structure of the heart rate time series and summarized different heartbeat modes in the time series. Based on the k-nearest neighbors (k-NN) classification algorithm, the probability of each heartbeat mode was used as a feature to detect and recognize stress caused by the driving environment. The results indicated that the stress from the driving environment changed the heartbeat mode. Stress-related heartbeat modes were determined, facilitating detection of the stress state with an accuracy of 93.7%. We also concluded that the heartbeat mode was correlated to the galvanic skin response (GSR) signal, reflecting real-time abnormal mood fluctuations. The proposed method revealed HRV characteristics that made quantifying and detecting different mental conditions possible. Thus, it would be feasible to achieve personalized analyses to further study the interaction between physiology and psychology.

In recent times, as interest in stress control has increased, many studies on stress recognition have been conducted. Several studies have been based on physiological signals, but the disadvantage of this strategy is that it requires physiological-signal-acquisition devices. Another strategy employs facial-image-based stress-recognition methods, which do not require devices, but predominantly use handcrafted features. However, such features have low discriminating power. We propose a deep-learning-based stress-recognition method using facial images to address these challenges. Given that deep-learning methods require extensive data, we constructed a large-capacity image database for stress recognition. Furthermore, we used temporal attention, which assigns a high weight to frames that are highly related to stress, as well as spatial attention, which assigns a high weight to regions that are highly related to stress. By adding a network that inputs the facial landmark information closely related to stress, we supplemented the network that receives only facial images as the input. Experimental results on our newly constructed database indicated that the proposed method outperforms contemporary deep-learning-based recognition methods.

In the field of stress recognition, the majority of research has conducted experiments on datasets collected from controlled environments with limited stressors. As these datasets cannot represent real-world scenarios, stress identification and analysis are difficult. There is a dire need for reliable, large datasets that are specifically acquired for stress emotion with varying degrees of expression for this task. In this paper, we introduced a dataset for Stress Analysis with Dimensions of Valence and Arousal of Korean Movie in Wild (SADVAW), which includes video clips with diversity in facial expressions from different Korean movies. The SADVAW dataset contains continuous dimensions of valence and arousal. We presented a detailed statistical analysis of the dataset. We also analyzed the correlation between stress and continuous dimensions. Moreover, using the SADVAW dataset, we trained a deep learning-based model for stress recognition.