Impact of Stress During COVID-19 Pandemic

The COVID-19 pandemic has affected our lives in many ways. Many people faced different challenges during the pandemic to accomplish their daily activities. Many people faced various challenges during the pandemic might have been very stressful, overwhelming, and disgusting. Therefore, it is common to feel stress, irritation, mood swings, and anxiety during the pandemic. Different methodologies by medical practitioners are being taken. Additionally, researchers from academia are also trying to strengthen the methods. Unfortunately, the way for automatic, continuous, and invisible stress detection by the researchers are insufficient and not studied in depth. It becomes essential in the post-pandemic scenario due to COVID-19 disease. This paper studies the impact of stress on people during the COVID-19 pandemic. The study includes origin, classification, impact on health, prevention solutions, etc. Further statistics on the affected people by the stress during the period are provided. © 2023 IEEE.

Computer assisted identification of stress, anxiety, depression (SAD) in students: A state-of-the-art review.

Stress, depression, and anxiety are a person's physiological states that emerge from various body features such as speech, body language, eye contact, facial expression, etc. Physiological emotion is a part of human life and is associated with psychological activities. Sad emotion is relatable to negative thoughts and recognized in three stages containing stress, anxiety, and depression. These stages of Physiological emotion show various common and distinguished symptoms. The present study explores stress, depression, and anxiety symptoms in student life. The study reviews the psychological features generated through various body parts to identify psychological activities. Environmental factors, including a daily routine, greatly trigger psychological activities. The psychological disorder may affect mental and physical health adversely. The correct recognition of such disorder is expensive and time-consuming as it requires accurate datasets of symptoms. In the present study, an attempt has been made to investigate the effectiveness of computerized automated techniques that include machine learning algorithms for identifying stress, anxiety, and depression mental disorder. The proposed paper reviews the machine learning-based algorithms applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. During the review process, the proposed study found that artificial intelligence and machine learning techniques are well recommended and widely utilized in most of the existing literature for measuring psychological disorders. The various machine learning-based algorithms are applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. There has been continuous monitoring for the body symptoms established in the various existing literature to identify psychological states. The present review reveals the study of excellence and competence of machine learning techniques in detecting psychological disorders' stress, depression, and anxiety parameters. This paper shows a systematic review of some existing computer vision-based models with their merits and demerits.

Stress detection and monitoring based on low-cost mobile thermography.

The high level of stress in modern life is one of the huge problems of the 21st century society, especially in the context of the Covid-19 pandemic. With the pandemic, the need for inexpensive, portable and easy-to-use health monitoring tools (mental and physical) has increased. Of particular importance here is mobile (smartphone) thermography, as it enables the initial detection and self-control of stress, which being intensified nowadays, is the cause of many diseases, depression and health problems. The smartphone thermal imaging camera responds to the strict sanitary guidelines, offering contact-free, painless and non-invasive operation. Additionally, it is included in the group of low-cost solutions available for home use. It is an alternative to commonly used (often expensive and unavailable to everyone): EMG, ECG, EEG, GSR or other high-cost stress detection tools. Thermal imaging by analyzing abnormalities or temperature changes allows for detection application. Therefore, the aim of this work is to determine the possibilities of a low-budget mobile thermal imaging camera in detecting stress, detecting and analyzing stress by identifying the characteristics of psychophysiological signals with the individual characteristics of the participants, along with the correlation. The participants' reactions to the film introducing stress tension up to the climax of the action were recorded thermographically. Data was processed in OpenCV. In the usual observation, stress often remained unnoticed. However, the thermographic analysis provided detailed information on the impact of the film's stressful situation on the participants, with the possibility of distinguishing the stages of stress. The results of the preliminary pilot study were presented, which indicated the variability of temperature and heart rate as important indicators of stress - with the simultaneous significance of individual characteristics of the participant. Smartphone stress thermography is a promising method of monitoring human stress, especially at home.

Stress Detection on Social Network: Public Mental Health Surveillance: Public mental health surveillance

Many nations of the world struggle with the COVID-19 pandemic, as the disease causes wide sweeping changes to society and the economy. One of the consequences of the pandemic is its effect on mental health stress. Gauging stress levels at scale is challenging to implement, as traditional methods require administrative labour and time. However, a combination of supervised Machine Learning (ML) and social media analytics could provide a faster and aggregated way to detect the stress levels of a population. This study investigates the potential clinical usage of ML practices for detecting stress in Twitter content, as a quantitative measure of stress at scale. The stress scores obtained by the models will be compared to the COVID-19 timeline of daily new cases. © 2023 ACM.

A Secure Wearable Framework for Stress Detection in Patients Affected by Communicable Diseases

The emergence of COVID-19 has drastically altered the lifestyle of people around the world, resulting in significant consequences on people's physical and mental well-being. Fear of COVID-19, prolonged isolation, quarantine, and the pandemic itself have contributed to a rise in hypertension among the general populace globally. Protracted exposure to stress has been linked with the onset of numerous diseases and even an increased frequency of suicides. Stress monitoring is a critical component of any strategy used to intervene in the case of stress. However, constant monitoring during activities of daily living using clinical means is not viable. During the current pandemic, isolation protocols, quarantines, and overloaded hospitals have made it physically challenging for subjects to be monitored in clinical settings. This study presents a proposal for a framework that uses unobtrusive wearable sensors, securely connected to an artificial intelligence (AI)-driven cloud-based server for early detection of hypertension and an intervention facilitation system. More precisely, the proposed framework identifies the types of wearable sensors that can be utilized ubiquitously, the enabling technologies required to achieve energy efficiency and secure communication in wearable sensors, and, finally, the proposed use of a combination of machine-learning (ML) classifiers on a cloud-based server to detect instances of sustained stress and all associated risks during times of a communicable disease epidemic like COVID-19. © 2001-2012 IEEE.

A systematic hybrid machine learning approach for stress prediction.

Stress is becoming an increasingly prevalent health issue, seriously affecting people and putting their health and lives at risk. Frustration, nervousness, and anxiety are the symptoms of stress and these symptoms are becoming common (40%) in younger people. It creates a negative impact on human lives and damages the performance of each individual. Early prediction of stress and the level of stress can help to reduce its impact and different serious health issues related to this mental state. For this, automated systems are required so they can accurately predict stress levels. This study proposed an approach that can detect stress accurately and efficiently using machine learning techniques. We proposed a hybrid model (HB) which is a combination of gradient boosting machine (GBM) and random forest (RF). These models are combined using soft voting criteria in which each model's prediction probability will be used for the final prediction. The proposed model is significant with 100% accuracy in comparison with the state-of-the-art approaches. To show the significance of the proposed approach we have also done 10-fold cross-validation using the proposed model and the proposed HB model outperforms with 1.00 mean accuracy and +/-0.00 standard deviation. In the end, a statistical T-test we have done to show the significance of the proposed approach in comparison with other approaches.

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.

An Efficient Classification Algorithm for Employee Well-Being Prediction Using Deep Learning

The impact of COVID-19 has changed the way work is being done especially in the IT sector. The emergence of work from home as an option has resulted in the evolution of hybrid work culture going forward as the world is moving towards endemic. On these circumstances there has been drastic change in work pattern of employees which clearly impacted the efficiency levels and their wellbeing (both physical and mental). It has also become imperative for the employers to track the efficiency of employees during their working hours in order to ensure maximum productivity in hybrid working model. This paper proposes a system that can detect and track the employee efficiency though facial landmarks by assessing the parameters like drowsiness and stress using deep learning techniques and hybridization of classification algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Stressors and Destressors in Working From Home Based on Context and Physiology From Self-Reports and Smartwatch Measurements: International Observational Study Trial.

BACKGROUND: The COVID-19 pandemic has greatly boosted working from home as a way of working, which is likely to continue for most companies in the future, either in fully remote or in hybrid form. To manage stress levels in employees working from home, insights into the stressors and destressors in a home office first need to be studied. OBJECTIVE: We present an international remote study with employees working from home by making use of state-of-the-art technology (ie, smartwatches and questionnaires through smartphones) first to determine stressors and destressors in people working from home and second to identify smartwatch measurements that could represent these stressors and destressors. METHODS: Employees working from home from 3 regions of the world (the United States, the United Kingdom, and Hong Kong) were asked to wear a smartwatch continuously for 7 days and fill in 5 questionnaires each day and 2 additional questionnaires before and after the measurement week. The entire study was conducted remotely. Univariate statistical analyses comparing variable distributions between low and high stress levels were followed by multivariate analysis using logistic regression, considering multicollinearity by using variance inflation factor (VIF) filtering. RESULTS: A total of 202 people participated, with 198 (98%) participants finishing the experiment. Stressors found were other people and daily life getting in the way of work (P=.05), job intensity (P=.01), a history of burnout (P=.03), anxiety toward the pandemic (P=.04), and environmental noise (P=.01). Destressors found were access to sunlight (P=.02) and fresh air (P<.001) during the workday and going outdoors (P<.001), taking breaks (P<.001), exercising (P<.001), and having social interactions (P<.001). The smartwatch measurements positively related to stress were the number of active intensity periods (P<.001), the number of highly active intensity periods (P=.04), steps (P<.001), and the SD in the heart rate (HR; P<.001). In a multivariate setting, only a history of burnout (P<.001) and family and daily life getting in the way of work (P<.001) were positively associated with stress, while self-reports of social activities (P<.001) and going outdoors (P=.03) were negatively associated with stress. Stress prediction models based on questionnaire data had a similar performance (F1=0.51) compared to models based on automatic measurable data alone (F1=0.47). CONCLUSIONS: The results show that there are stressors and destressors when working from home that should be considered when managing stress in employees. Some of these stressors and destressors are (in)directly measurable with unobtrusive sensors, and prediction models based on these data show promising results for the future of automatic stress detection and management. TRIAL REGISTRATION: Netherlands Trial Register NL9378; https://trialsearch.who.int/Trial2.aspx?TrialID=NL9378.

COVID-19 Impact on Online Learning: A Statistical and Machine Learning Model Analysis for Stress Detection

The preventive measure to control the outbreak of COVID-19 pandemic compelled the government across the globe to close the educational premises. In order to fill the academic gap, and to follow the prescribed isolation in this outbreak, a shift of physical classroom interaction to virtual space becomes indispensable. This rushed shift is largely affecting the academicians, groups of students, and institutions. Although, students from different backgrounds may have different psychological impacts of online learning experiences depending upon their usage and comfortability with the e-learning technology. Many researchers reported that online learning has resulted in depression and anxiety disorder among students and in due course resulting in increased stress levels. Therefore, it is vital to comprehend and examine the impact of this unexpected shift in the learning environment on students’ psychology and stress levels. There are numerous studies associated with stress identification in controlled laboratory surroundings, but there is inadequate research related to stress measurement in general (Can et al. in J. Biomed. Inform. 92, 2019). The present study is an attempt to explore a variety of predictive analysis and statistical analysis techniques in identification of stress and also to analyze the perceived stress level of students in online learning experience using cross-sectional research. The data collected is analyzed in depth using the regression analysis and the relationship between various factors is established and other valuable insights are demonstrated. The outcome of this research would be helpful for the educational institutes, policymakers, and government bodies to appraise the challenges and the inadequacies of online teaching platforms and their impact on student’s stress levels. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

X-ECGNet: An Interpretable DL model for Stress Detection using ECG in COVID-19 Healthcare Workers

COVID-19 pandemic erupted in December 2019, spreading extremely fast and stretching the healthcare infras-tructure of most countries beyond their capacities. This impacted the healthcare workers (HCW) adversely because 1) they were pressured to work almost round the clock without a break;2) they were in close contact with the COVID-19 patients and hence, were at high risk;and 3) they suffered from the fear of spreading COVID to their families. Hence, many HCWs were stressed and burnout. It is known that stress directly affects the heart and can lead to serious cardiovascular problems. Currently, stress is measured subjectively via self-declared questionnaires. Objective markers of stress are required to ascertain the quantitative impact of stress on the heart. Thus, this paper aims to detect stress contributing factors in HCWs and determine the changes in the ECG of stressed HCWs. We collected data from multiple hospitals in Northern India and developed a deep learning model, namely X-ECGNet, to detect stress. We also tried to add interpretability to the model using the recent method of SHAP analysis. Deployment of such models can help the government and hospital administrations timely detect stress in HCWs and make informed decisions to save systems from collapse during such calamities. © 2021 IEEE.

Personalized Stress Monitoring AI System for Healthcare Workers

In the current COVID-19 pandemic scenario, healthcare workers, in particular nurses, face prolonged exposure to stress. This intense duress takes a toll on their health overtime, affects their quality of life, and in turn impacts the quality of care provided to the patients. Hence, real-time detection and monitoring of stress is extremely important for early detection of stress patterns, prevention of burnouts and chronic conditions in healthcare workers as well as facilitate improved patient-care outcomes. In this paper, we present a proof-of-concept case study using machine learning (ML) and artificial intelligence (AI)-based stress detection model that determines a personalized assessment of stress level using heart rate, heart rate variability, and physical activity of the users. We used wearable electrocardiogram and inertial sensor to record heart activity and physical activity of nurses during their shifts. Our preliminary results indicate that the proposed stress tracking model can effectively predict any stress occurrences. This study is a pivotal attempt to emphasize the significance of stress-detection and relief for healthcare workers and provide them a tool for an effective assessment of personalized stress levels. © 2021 IEEE.

Stress Analysis and Care Prediction System for Online Workers

Working from home (WFH) online during the covid-19 pandemic has caused increased stress level. Online workers/students have been affecting by the crisis according to new researches. Natural response of body, to external and internal stimuli is stress. Even though stress is a natural occurrence, prolonged exposure while working Online to stressors can lead to serious health problems if any action will not be applied to control it. Our research has been conducted deeply to identify the best parameters, which have connection with stress level of online workers. As a result of our research, a desktop application has been created to identify the users stress level in real time. According to the results, our overall system was able to provide outputs with more than 70% accuracy. It will give best predictions to avoid the health problems. Our main goal is to provide best solution for the online workers to have healthy lifestyles. Updates for the users will be provided according to the feedback we will have in the future from the users. Our System will be a most valuable application in the future among online workers. © 2021 IEEE.

Measuring Stress in Health Professionals Over the Phone Using Automatic Speech Analysis During the COVID-19 Pandemic: Observational Pilot Study.

BACKGROUND: During the COVID-19 pandemic, health professionals have been directly confronted with the suffering of patients and their families. By making them main actors in the management of this health crisis, they have been exposed to various psychosocial risks (stress, trauma, fatigue, etc). Paradoxically, stress-related symptoms are often underreported in this vulnerable population but are potentially detectable through passive monitoring of changes in speech behavior. OBJECTIVE: This study aims to investigate the use of rapid and remote measures of stress levels in health professionals working during the COVID-19 outbreak. This was done through the analysis of participants' speech behavior during a short phone call conversation and, in particular, via positive, negative, and neutral storytelling tasks. METHODS: Speech samples from 89 health care professionals were collected over the phone during positive, negative, and neutral storytelling tasks; various voice features were extracted and compared with classical stress measures via standard questionnaires. Additionally, a regression analysis was performed. RESULTS: Certain speech characteristics correlated with stress levels in both genders; mainly, spectral (ie, formant) features, such as the mel-frequency cepstral coefficient, and prosodic characteristics, such as the fundamental frequency, appeared to be sensitive to stress. Overall, for both male and female participants, using vocal features from the positive tasks for regression yielded the most accurate prediction results of stress scores (mean absolute error 5.31). CONCLUSIONS: Automatic speech analysis could help with early detection of subtle signs of stress in vulnerable populations over the phone. By combining the use of this technology with timely intervention strategies, it could contribute to the prevention of burnout and the development of comorbidities, such as depression or anxiety.