Predicted Stress Levels Research Articles

Microstructural analysis of AA8079 alloy sheets formed by friction stir incremental forming

Friction Stir Incremental Forming (FSIF) is an innovative solid-state metal-forming technique that uses frictional heat and mechanical deformation to create complex shapes without dyes. It has found applications in industries like aerospace, automotive, and consumer goods. In this study, FSIF was applied to AA8079 alloy sheets to form cone and pyramid geometries. A detailed investigation of process parameters such as spindle speed, table feed, step size, and coatings was conducted to assess their effects on surface roughness and forming forces. Optimal parameters were identified using the Taguchi L27 orthogonal array and the TOPSIS method, which determined the best settings as a wall angle of 20°, a step size of 1 mm, a spindle speed of 1500 rpm, and a table feed of 2400 mm/min. Scanning electron microscopy was employed to study surface roughness, texture, and defects, revealing the influence of these parameters on surface quality. Additionally, Finite Element Method (FEM) analysis, performed using ABAQUS software, was used to predict formability and stress distribution during the forming process. Key results showed successful formation of cone and triangular pyramid shapes on AA8079 sheets, with FEM accurately predicting stress levels. This comprehensive study provides valuable insights into optimizing FSIF for improved material performance and structural integrity. The findings emphasize the importance of fine-tuning FSIF parameters to enhance surface finish and reduce defects, offering potential improvements for various industrial applications.

Gradient One-to-One Optimizer and Deep Learning based Student Stress Level Prediction Model

Gradient One-to-One Optimizer and Deep Learning based Student Stress Level Prediction Model

An enhanced fatigue damage model based on strength degradation of composite materials

AbstractAn enhanced nonlinear fatigue damage cumulative model proposed is based on the strength degradation characteristics of composites, aiming to investigate damage progression under fatigue loading. Building upon this foundation, given the assumption of a linear correlation between fatigue cumulative damage and stress level, a methodology is presented for extrapolating the damage curve of untested stress levels from that of tested stress levels. The model substantiates its reliability by validating against experimental data from several distinct material types. The reliability of the model has been validated using experimental data from multiple groups of materials. The experimental results indicate that the model can effectively reflect the fatigue damage development characteristics of composite materials. Simultaneously, the predicted stress levels derived from the proposed methodology show lesser deviation from the fitted data. Finally, a life prediction method founded on the proposed model is introduced, validated for its high prediction accuracy through experimentation.

Predicting Workers' Stress: Application of a High-Performance Algorithm Using Working-Style Characteristics.

Work characteristics, such as teleworking rate, have been studied in relation to stress. However, the use of work-related data to improve a high-performance stress prediction model that suits an individual's lifestyle has not been evaluated. This study aims to develop a novel, high-performance algorithm to predict an employee's stress among a group of employees with similar working characteristics. This prospective observational study evaluated participants' responses to web‑based questionnaires, including attendance records and data collected using a wearable device. Data spanning 12 weeks (between January 17, 2022, and April 10, 2022) were collected from 194 Shionogi Group employees. Participants wore the Fitbit Charge 4 wearable device, which collected data on daily sleep, activity, and heart rate. Daily work shift data included details of working hours. Weekly questionnaire responses included the K6 questionnaire for depression/anxiety, a behavioral questionnaire, and the number of days lunch was missed. The proposed prediction model used a neighborhood cluster (N=20) with working-style characteristics similar to those of the prediction target person. Data from the previous week predicted stress levels the following week. Three models were compared by selecting appropriate training data: (1) single model, (2) proposed method 1, and (3) proposed method 2. Shapley Additive Explanations (SHAP) were calculated for the top 10 extracted features from the Extreme Gradient Boosting (XGBoost) model to evaluate the amount and contribution direction categorized by teleworking rates (mean): low: <0.2 (more than 4 days/week in office), middle: 0.2 to <0.6 (2 to 4 days/week in office), and high: ≥0.6 (less than 2 days/week in office). Data from 190 participants were used, with a teleworking rate ranging from 0% to 79%. The area under the curve (AUC) of the proposed method 2 was 0.84 (true positive vs false positive: 0.77 vs 0.26). Among participants with low teleworking rates, most features extracted were related to sleep, followed by activity and work. Among participants with high teleworking rates, most features were related to activity, followed by sleep and work. SHAP analysis showed that for participants with high teleworking rates, skipping lunch, working more/less than scheduled, higher fluctuations in heart rate, and lower mean sleep duration contributed to stress. In participants with low teleworking rates, coming too early or late to work (before/after 9 AM), a higher/lower than mean heart rate, lower fluctuations in heart rate, and burning more/fewer calories than normal contributed to stress. Forming a neighborhood cluster with similar working styles based on teleworking rates and using it as training data improved the prediction performance. The validity of the neighborhood cluster approach is indicated by differences in the contributing features and their contribution directions among teleworking levels. UMIN UMIN000046394; https://www.umin.ac.jp/ctr/index.htm.

Prediction of stress level in military submariners, based on physical conditioning and sleep quality variables

The objective of the study was to predict the level of stress in military submariners based on physical fitness and sleep quality variables. Cross-sectional and correlational research, with a sample of 40 male submariners (28.70±3.98 years old). Body mass and composition data were analyzed (MCT), height, BMI, waist, body fat (%G), dominant handgrip (PMDom), cardiorespiratory fitness (VO2max), squats, sit-ups, relative power of lower limbs (PotRel MMII), stress (PSS-10), Epworth Sleepiness Assessment (ESE) and Pittsburgh Sleep Quality Index (PSQI). Overall, negative associations (p&lt;0.05) were observed between VO2max and MCT, BMI, %BF, and waist; PotRel MMII and age and %BF; squat and %G; sit-up and MCT, BMI, %BF and waist; and finally, stress and VO2max and sit-up. Furthermore, positive associations (p&lt;0.05) between BMI and MCT; %BF and MCT and BMI; waist and age, BMI, MCT, and %BF; squats and PMdom., VO2max and PotRel MMII; sit-ups and VO2max, PotRel LL and squats; ending with PSQI and stress. Furthermore, among the stress level prediction equation models developed, the equation Stress=15.92+1.538(PSQI)-0.497(VO2max)+0.188(Squat) met the proposed requirements, which is based on sleep quality, cardiorespiratory fitness, and number of squats. Keywords: Submariner; stress, physical fitness, sleep quality, military personal.

Analysis of support vector machine and random forest models for classification of the impact of technostress in covid and post-covid era

This study addresses the growing concern of technostress, a condition caused by the overwhelming use of digital technologies, exacerbated by the COVID-19 pandemic. The researchers developed a predictive model using machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), to assess and manage technostress levels. The model considers factors such as age, gender, technology usage hours, and technological experiences to classify stress levels into high, moderate, and low categories. The study collected data through a questionnaire administered to knowledgeable respondents, using a non-probabilistic sampling approach. The results showed that both RF and SVM algorithms achieved high accuracy in classifying technostress, with SVM performing slightly better (94.5% vs 84.50%). The model’s effectiveness in predicting stress levels for users with varying degrees of stress is a significant contribution to the field. The research also developed an interactive user interface to facilitate user engagement with the model, promoting stress management and well-being in a technology-driven society. The study’s findings provide valuable insights into the challenges posed by technostress and offer a solution for mitigating its effects. The use of machine learning algorithms to classify gender based on the dataset demonstrates the model’s potential applications in various areas. Overall, this study demonstrates the importance of addressing technostress in the digital age and provides a valuable tool for managing stress levels. The development of predictive models like this one can help individuals and organizations mitigate the negative impacts of technostress, promoting a healthier and more sustainable relationship with technology.

A Deep Learning Approach to Estimate Multi-Level Mental Stress From EEG Using Serious Games.

Stress is revealed by the inability of individuals to cope with their environment, which is frequently evidenced by a failure to achieve their full potential in tasks or goals. This study aims to assess the feasibility of estimating the level of stress that the user is perceiving related to a specific task through an electroencephalograpic (EEG) system. This system is integrated with a Serious Game consisting of a multi-level stress driving tool, and Deep Learning (DL) neural networks are used for classification. The game involves controlling a vehicle to dodge obstacles, with the number of obstacles increasing based on complexity. Assuming that there is a direct correlation between the difficulty level of the game and the stress level of the user, a recurrent neural network (RNN) with a structure based on gated recurrent units (GRU) was used to classify the different levels of stress. The results show that the RNN model is able to predict stress levels above current state-of-the-art with up to 94% accuracy in some cases, suggesting that the use of EEG systems in combination with Serious Games and DL represents a promising technique in the prediction and classification of mental stress levels.

BODY AWARENESS, STRESS AND SYMPTOMS IN AUTONOMIC DYSFUNCTION IN PATIENTS WITH CHRONIC PAIN: AN EXPLORATIVE STUDY.

To assess pain outcomes, stress levels and body awareness among patients with chronic pain and explore potential associations between these variables. An explorative study. Patients with chronic pain in primary and specialist care were assessed regarding pain intensity using the Numerical Rating Scale (NRS; 0-10 point scale) and stress levels using the Stress and Crisis Inventory (SCI-93; 0-140). To assess body awareness, multidimensional assessment of interoceptive awareness (MAIA; 0-5), a widely used self-report measure of interoceptive bodily awareness was used. Participants (n = 42) reported an average NRS of 4.4, elevated stress levels and low body awareness. Stress levels were moderately correlated with pain intensity (r = 0.53; p < 0.001; 95% confidence interval [CI] 0.25-0.72) and number of pain sites (r = 0.58; p < 0.001; 95% CI 0.32-0.76). The regression analysis showed that pain outcomes predicted stress level scores and explained almost 50% of variance (R 2 = 0.47, p < 0.001). Moreover, shorter pain duration predicted a higher body awareness (p = 0.04). In patients with chronic pain, high pain intensity and multiple painful sites seem to be associated with impaired stress regulation. The patients had low body awareness, which was negatively influenced by pain duration.

Mental Stress Prediction Using Machine Learning on Real Time Dataset

Mental stress is a significant issue affecting individuals across all age groups in today's society. The impact of stress extends beyond mental well-being and can lead to various chronic illnesses, including depression, malignancy, and cardiovascular disease (CVD). Addressing mental stress and implementing strategies for stress management and prevention are crucial not only for mental well-being but also for reducing the risk of associated chronic illnesses. In this paper, we are focussing of the problem of mental stress in today's society and the significance of early prediction and management. The use of Random Forest (RF) method with enhanced band pass filtration technique on ECG data for predicting stress levels is a promising approach. Achieving a 96.73% accuracy in stress categorization, along with improvements over prior research results, highlights the effectiveness of the proposed model. The main contribution of the research work is that the results of the model are validated using real-time datasets which further strengthens its reliability and applicability in practical scenarios.

Exploring occupational stress among employees in the financial industry – A perspective from developing economies in Addis Ababa, Ethiopia

The study aims in analyzing the role of workplace stressors on occupational stress levels. A descriptive and explanatory research design has been used. A mean scale as a measure of central tendency was used to explain the existing situation. Correlation and multiple regression have been implemented to measure the association of variables and the stress level prediction ability of the constructs. A random sampling technique was used in determining the sample size. Mainly primary data is collected through a structured questionnaire and distributed to people online through Google form. Occupational stresses have shown a strong and significant association with stress constructs. The cumulative prediction ability of the constructs is stronger on the occupational stress level of the organization. Social support and job control are inversely associated with occupational stress levels. However. role ambiguity and role overload have a direct relationship with the magnitude of occupational stress.

Dynamic clustering via branched deep learning enhances personalization of stress prediction from mobile sensor data

College students experience ever-increasing levels of stress, leading to a wide range of health problems. In this context, monitoring and predicting students’ stress levels is crucial and, fortunately, made possible by the growing support for data collection via mobile devices. However, predicting stress levels from mobile phone data remains a challenging task, and off-the-shelf deep learning models are inapplicable or inefficient due to data irregularity, inter-subject variability, and the “cold start problem”. To overcome these challenges, we developed a platform named Branched CALM-Net that aims to predict students’ stress levels through dynamic clustering in a personalized manner. This is the first platform that leverages the branching technique in a multitask setting to achieve personalization and continuous adaptation. Our method achieves state-of-the-art performance in predicting student stress from mobile sensor data collected as part of the Dartmouth StudentLife study, with a ROC AUC 37% higher and a PR AUC surpassing that of the nearest baseline models. In the cold-start online learning setting, Branched CALM-Net outperforms other models, attaining an average F1 score of 87% with just 1 week of training data for a new student, which shows it is reliable and effective at predicting stress levels from mobile data.

Prediction of stress levels in sleep patterns based on random forest

The prevalence of stress in contemporary society has emerged as a significant concern, exerting a profound influence on our daily lives. The objective of this study is to predict stress levels in sleep patterns through the utilization of a machine learning algorithm known as random forest. The significance of stress detection has increased due to its potential to induce various issues such as insomnia and depression. The examination of stress can assist individuals in mitigating the adverse effects associated with prolonged exposure to stress. The study commences with the preprocessing phase, followed by an exploratory data analysis, subsequent dataset splitting, identification of significant features, and concludes with model training. The utilization of the random forest model can enhance the comprehension of the association between sleeping characteristics and levels of stress. Furthermore, it produces a f1-score of 98 percent, indicating a strong predictive capability for determining stress levels in sleep patterns. The proposed method can effectively predict stress levels during sleep mode. This study can provide an effective model for society to prevent people's psychological problems in advance.

My Colleague and I: Contribution of Coworker Support and Self-compassion to Stress Among Health Workers During Health Crises

The devastating Covid-19 pandemic forced health workers to adapt quickly to uncertain circumstances, increasing moral burden, excessive workload, and limited rest time. Many adversities in the hospital setting may increase stress levels due to prolonged and uncertain pandemics. Coworker support and self-compassion are two protective factors that may help health workers to manage stress during the pandemic. This study examined the contribution of coworker support and self-compassion to stress among health workers during the pandemic. A quantitative correlational study using simple random sampling to select samples from a list of referral hospitals for Covid-19 in the Malang area. One hundred seventeen health workers from six randomly selected referral hospitals for Covid-19 completed three instruments: the stress subscale of Depression, Anxiety, Stress Scale 21 (DASS-21), Coworker Support Scale, and Self- Compassion Scale (SWD) adapted to the Indonesian population. The linear regression showed that only self-compassion predicted stress levels among health workers. In contrast, coworker support and self-compassion did not contribute simultaneously to the stress levels of health workers during the pandemic. Therefore, maintaining self-compassion is vital to coping with stress while providing adequate health care during health crises.&#x0D; Keywords: coworker support, health worker, self-compassion, stress

Prediction of driving stress on high-altitude expressway using driving environment features: A naturalistic driving study in Tibet

Objective Owing to the harsh environment in high-altitude areas, drivers experience significant driving stress. Compared with urban roads or expressways in low-altitude areas, the driving environment in high-altitude areas has distinct features, including mountainous environments and a higher proportion of trucks and buses. This study aims to investigate the feasibility of predicting stress levels through elements in the driving environment. Methods Naturalistic driving tests were conducted on an expressway in Tibet. Driving stress was assessed using heart rate variability (HRV)-based indicators and classified using K-means clustering. A DeepLabv3 model was built to conduct semantic segmentation and extract environment elements from the driving scenarios recorded through a camera next to the driver’s eyes. A decision tree and 4 other ensemble learning models based on decision trees were built to predict driving stress levels using the environment elements. Results Fifty-six indicators were extracted from the driving environment. Results of the prediction models demonstrate that extreme gradient boosting has the best overall performance with the F1 score (harmonic mean of the precision and recall) and G-mean (geometric mean of sensitivity and specificity) reaching 0.855 and 0.890, respectively. Indicators based on the variation rate of trucks and buses have high feature importance and exhibit positive effects on driving stress. Indicators reflecting the proportion of mountain, road, and sky features negatively affect the expected levels of driving stress. Additionally, the mountain feature demonstrates multidimensional effects, because driving stress is positively affected by indicators of the variation rate for mountain elements. Conclusions This study validates the prediction of driving stress using environment elements in the driver’s field of view and extends its application to high-altitude expressways with distinct environmental characteristics. This method provides a real-time, less intrusive, and safer method of driving stress assessment and prediction and also enhances the understanding of the environmental determinants of driving stress. The results hold promising applications, including the development of a driving state assessment and warning module as well as the identification of high-risk road sections and implementation of control measures.

Federated clustered multi-domain learning for health monitoring

Wearable Internet of Things (WIoT) and Artificial Intelligence (AI) are rapidly emerging technologies for healthcare. These technologies enable seamless data collection and precise analysis toward fast, resource-abundant, and personalized patient care. However, conventional machine learning workflow requires data to be transferred to the remote cloud server, which leads to significant privacy concerns. To tackle this problem, researchers have proposed federated learning, where end-point users collaboratively learn a shared model without sharing local data. However, data heterogeneity, i.e., variations in data distributions within a client (intra-client) or across clients (inter-client), degrades the performance of federated learning. Existing state-of-the-art methods mainly consider inter-client data heterogeneity, whereas intra-client variations have not received much attention. To address intra-client variations in federated learning, we propose a federated clustered multi-domain learning algorithm based on ClusterGAN, multi-domain learning, and graph neural networks. We applied the proposed algorithm to a case study on stress-level prediction, and our proposed algorithm outperforms two state-of-the-art methods by 4.4% in accuracy and 0.06 in the F1 score. In addition, we demonstrate the effectiveness of the proposed algorithm by investigating variants of its different modules.

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.

Advanced machine learning models for Depression level categorization using DSM 5 and personality traits

The COVID-19 pandemic has led to an increase in mental health problems, such as depression. Depression is a major cause of disability and can affect anyone. It is important to seek early detection and treatment for depression, as it can have a significant impact on a person’s life. This research paper explores the complex and multifaceted nature of human personality using the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria and Eysenck’s personality traits questionnaire. The paper then outlines the methodology employed, involving the collection of data through surveys consisting of 66 questions divided into six forms. DSM-5 criteria are applied to assess depressive episodes, while Eysenck’s questionnaire evaluates personality traits. The paper’s approach involves two stages: predicting personality traits and estimating stress levels. Multiple machine learning models are utilized for these predictions. The results of this study highlight the effectiveness of the Support Vector Machine (SVM) classifier, which consistently outperforms other models, achieving impressive accuracy in predicting both personality traits, such as extroversion and neuroticism, and stress levels. Notably, SVM demonstrates its prowess with an accuracy of 91.43% in predicting stress levels.

DeepChill: ECG Analysis using Deep Learning for Automatic Stress Recognition

Stress is a common experience with significant impacts on our health, which has sparked an increase in research aimed at detecting stress. This paper explores the effectiveness of using Electrocardiogram (ECG) signals as a key indicator for stress detection. What distinguishes our research is the carefully constructed architecture of our deep neural network model, coupled with the meticulous optimization strategies employed. We trained and evaluated our model using the comprehensive WESAD[10] dataset, which includes PANAS[10] self-reports. Our proposed model demonstrates remarkable improvements in accuracy and F1 score compared to previous attempts, highlighting the potential of deep learning techniques in understanding and predicting stress levels based on physiological data and harness its power to offer an objective measure of the body's response to stress. The results of the DeepChill demonstrate the effectiveness of the deep learning model in detecting stress from ECG signals, highlighting its potential for health monitoring and intervention. This contributes to the field of affective computing by providing a reliable and efficient method for stress detection. The proposed fully connected neural network model has attained an impressive accuracy of 92% surpassing the 85% accuracy reported on the WESAD dataset.

PENGARUH GROWTH MINDSET TERHADAP STRES AKADEMIK MAHASISWA

Students who have a growth mindset tend to be able to face academic challenges and demands. This study aims to determine the effect of a growth mindset on students' academic stress levels. This research method uses a quantitative paradigm with a correlational approach to determine the relationship between a growth mindset and students' academic stress levels. The data collection technique uses two types of scales, namely the growth mindset scale and perceived academic stress (PAS). The subjects in this study were active students of the class of 2020 and 2021 at the Faculty of Education and Psychology, Yogyakarta State University. The results of this study indicate a relationship between a growth mindset and academic stress where the higher the level of a growth mindset of students, the lower the predicted stress level. The regression test results show a coefficient of 0.066, which means that 6.6% of the factors that influence a growth mindset are academic stress, and the rest is influenced by other factors.

A Multimodal Late Fusion Framework for Physiological Sensor and Audio-Signal-Based Stress Detection: An Experimental Study and Public Dataset

Stress can be considered a mental/physiological reaction in conditions of high discomfort and challenging situations. The levels of stress can be reflected in both the physiological responses and speech signals of a person. Therefore the study of the fusion of the two modalities is of great interest. For this cause, public datasets are necessary so that the different proposed solutions can be comparable. In this work, a publicly available multimodal dataset for stress detection is introduced, including physiological signals and speech cues data. The physiological signals include electrocardiograph (ECG), respiration (RSP), and inertial measurement unit (IMU) sensors equipped in a smart vest. A data collection protocol was introduced to receive physiological and audio data based on alterations between well-known stressors and relaxation moments. Five subjects participated in the data collection, where both their physiological and audio signals were recorded by utilizing the developed smart vest and audio recording application. In addition, an analysis of the data and a decision-level fusion scheme is proposed. The analysis of physiological signals includes a massive feature extraction along with various fusion and feature selection methods. The audio analysis comprises a state-of-the-art feature extraction fed to a classifier to predict stress levels. Results from the analysis of audio and physiological signals are fused at a decision level for the final stress level detection, utilizing a machine learning algorithm. The whole framework was also tested in a real-life pilot scenario of disaster management, where users were acting as first responders while their stress was monitored in real time.