Stress Detection Methods Research Articles

Research on Pipeline Stress Detection Method Based on Double Magnetic Coupling Technology.

Oil and gas pipelines are subject to soil corrosion and medium pressure factors, resulting in stress concentration and pipe rupture and explosion. Non-destructive testing technology can identify the stress concentration and defect corrosion area of the pipeline to ensure the safety of pipeline transportation. In view of the problem that the traditional pipeline inspection cannot identify the stress signal at the defect, this paper proposes a detection method using strong and weak magnetic coupling technology. Based on the traditional J-A (Jiles-Atherton) model, the pinning coefficient is optimized and the stress demagnetization factor is added to establish the defect of the ferromagnetic material. The force-magnetic relationship optimization model is used to calculate the best detection magnetic field strength. The force-magnetic coupling simulation of Q235 steel material is carried out by ANSYS 2019 R1 software based on the improved J-A force-magnetic model. The results show that the effect of the stress on the pipe on the magnetic induction increases first and then decreases with the increase in the excitation magnetic field strength, and the magnetic signal has the maximum proportion of the stress signal during the excitation process; the magnetic induction at the pipe defect increases linearly with the increase in the stress trend. Through the strong and weak magnetic scanning detection of cracked pipeline materials, the correctness of the theoretical analysis and the validity of the engineering application of the strong and weak magnetic detection method are verified.

A New Era in Stress Monitoring: A Review of Embedded Devices and Tools for Detecting Stress in the Workplace

Detection of stress and the development of innovative platforms for stress monitoring have attracted significant attention in recent years due to the growing awareness of the harmful effects of stress on mental and physical health. Stress is a widespread issue affecting individuals and often goes unnoticed as a health concern. It can lead to various negative physiological conditions, including anxiety, depression, cardiovascular diseases and cognitive impairments. The aim of this paper is to provide an overview of studies focusing on embedded devices for non-invasive stress detection, primarily in the form of a modified computer mouse or keyboard. This study not only fills a critical gap in the literature but also provides valuable insights into the design and implementation of hardware-based stress-detection methods. By focusing on embedded devices, specifically computer peripherals, this research highlights the potential for integrating stress monitoring into everyday workplace tools, thereby offering practical solutions for improving occupational health and well-being.

Electromagnetic Acoustic Transducer and Method for Residual Stress Detection in Conducting Materials

Electromagnetic Acoustic Transducer and Method for Residual Stress Detection in Conducting Materials

Contact stress ultrasonic detection method based on contact acoustic resistance model

The size and distribution of the contact stress at the assembly interface will directly affect the connection performance of the mechanical joint. However, the rough assembly interface presents strong dielectric discontinuity and material nonlinearity, which become the bottleneck restricting the development of contact stress detection. How to obtain contact stress by nondestructive testing means has become an urgent problem to be solved. In this paper, a multi-layer interface contact stress detection method based on contact acoustic resistance model is proposed to solve the difficult problem of rough interface contact stress detection. Firstly, based on the formation mechanism of contact acoustic resistance of rough assembly interface, the equivalent contact acoustic resistance expression of contact layer is derived. Secondly, the contact mechanism of rough assembly interface is clarified, and the mapping relationship model between contact acoustic resistance and contact stress is constructed. On this basis, according to the transmission law of ultrasonic wave at different interfaces of multilayer acoustic impedance, the expression of acoustic transmission coefficient at the interface of variable acoustic impedance is obtained. A contact stress detection method based on contact stress-contact acoustic resistance coefficient and acoustic transmission coefficient is proposed. The correctness of the theoretical model is verified by the tensile testing machine, and the maximum error is 15.52 %. This method will provide powerful data support for the evaluation of assembly interface connection state.

Stress detection method of pipeline based on vibration characteristics

Stress concentration during the operation of long-distance transmission pipelines is an inevitable problem. In this paper, a pipeline stress detection method based on vibration characteristics is proposed. We establish a finite element model for knock detection and determine the law of vibration frequency and pipeline stress. The reliability of the model is verified by the knock detection experiments. It is found that the natural frequency stress of the vibration of the sample shows a close correlation under the knocking effect. The study results provide an innovative process for pipeline stress detection.

Improved method for stress detection using bio-sensor technology and machine learning algorithms

Maintaining an optimal stress level is vital in our lives, yet many individuals struggle to identify the sources of their stress. As emotional stability and mental awareness become increasingly important, wearable medical technology has gained popularity in recent years. This technology enables real-time monitoring, providing medical professionals with crucial physiological data to enhance patient care. Current stress-detection methods, such as ECG, BVP, and body movement analysis, are limited by their rigidity and susceptibility to noise interference. To overcome these limitations, we introduce STRESS-CARE, a versatile stress detection sensor employing a hybrid approach. This innovative system utilizes a sweat sensor, cutting-edge context identification methods, and machine learning algorithms. STRESS-CARE processes sensor data and models environmental fluctuations using an XG Boost classifier. By combining these advanced techniques, we aim to revolutionize stress detection, offering a more adaptive and robust solution for improved stress management and overall well-being.•In the proposed method, we introduce a state-of-the-art stress detection device with Galvanic Skin Response (GSR) sweat sensors, outperforming traditional Electrocardiogram (ECG) methods while remaining non-invasive•Integrating machine learning, particularly XG-Boost algorithms, enhances detection accuracy and reliability.•This study sheds light on noise context comprehension for various wearable devices, offering crucial guidance for optimizing stress detection in multiple contexts and applications.

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.

Explainable stress type classification captures physiologically relevant responses in the Maastricht Acute Stress Test.

Current stress detection methods concentrate on identification of stress and non-stress states despite the existence of various stress types. The present study performs a more specific, explainable stress classification, which could provide valuable information on the physiological stress reactions. Physiological responses were measured in the Maastricht Acute Stress Test (MAST), comprising alternating trials of cold pressor (inducing physiological stress and pain) and mental arithmetics (eliciting cognitive and social-evaluative stress). The responses in these subtasks were compared to each other and to the baseline through mixed model analysis. Subsequently, stress type detection was conducted with a comprehensive analysis of several machine learning components affecting classification. Finally, explainable artificial intelligence (XAI) methods were applied to analyze the influence of physiological features on model behavior. Most of the investigated physiological reactions were specific to the stressors, and the subtasks could be distinguished from baseline with up to 86.5% balanced accuracy. The choice of the physiological signals to measure (up to 25%-point difference in balanced accuracy) and the selection of features (up to 7%-point difference) were the two key components in classification. Reflection of the XAI analysis to mixed model results and human physiology revealed that the stress detection model concentrated on physiological features relevant for the two stressors. The findings confirm that multimodal machine learning classification can detect different types of stress reactions from baseline while focusing on physiologically sensible changes. Since the measured signals and feature selection affected classification performance the most, data analytic choices left limited input information uncompensated.

Stress recognition from facial images in children during physiotherapy with serious games

Serious games have emerged as promising tools for physiotherapy, offering an engaging way to motivate patients and improve physical functions. Stress, a common physiological response, can impede patient performance and hinder recovery in physiotherapy. However, there is limited research on stress recognition for children during physiotherapeutic serious games. This study proposes a deep and machine learning based method for stress detection from facial images of children. The contributions include the utilization of a novel dataset comprising videos of 25 children, including children with specific conditions, and the exploration of using adults’ facial images for data augmentation. The proposed method employs a modified deep network architecture, VGG-Face, for facial emotion recognition and utilizes three machine learning models for stress recognition. This study represents the first attempt to recognize stress in children’s facial images during physiotherapy with serious games. The findings hold the potential to optimize patient outcomes and contribute to monitoring patients during home therapy.

Mental stress recognition on the fly using neuroplasticity spiking neural networks

Mental stress is found to be strongly connected with human cognition and wellbeing. As the complexities of human life increase, the effects of mental stress have impacted human health and cognitive performance across the globe. This highlights the need for effective non-invasive stress detection methods. In this work, we introduce a novel, artificial spiking neural network model called Online Neuroplasticity Spiking Neural Network (O-NSNN) that utilizes a repertoire of learning concepts inspired by the brain to classify mental stress using Electroencephalogram (EEG) data. These models are personalized and tested on EEG data recorded during sessions in which participants listen to different types of audio comments designed to induce acute stress. Our O-NSNN models learn on the fly producing an average accuracy of 90.76% (σ = 2.09) when classifying EEG signals of brain states associated with these audio comments. The brain-inspired nature of the individual models makes them robust and efficient and has the potential to be integrated into wearable technology. Furthermore, this article presents an exploratory analysis of trained O-NSNNs to discover links between perceived and acute mental stress. The O-NSNN algorithm proved to be better for personalized stress recognition in terms of accuracy, efficiency, and model interpretability.

Method for Stress Detection in Industrial Assemblers Using Smartwatch Sensors

Goal: The purpose of this study is to present a method to quantitatively identify the levels of stress of employees during work using the technology of smart watches with sensors measuring vital signs and, from this data, deepen the discussion on the relationships between the levels of physical and cognitive complexity of the activities and their influence on the stress resulting from these works. 
 
 Theoretical reference: Use of literature through research on similar methods already applied in immersive training, dissertations, articles, and national standards. For this purpose, the Systematic Bibliographic Review (SBR) was used, which is “a scientific method for searching and analyzing articles in a given area of ​​science”. 
 
 Method: To achieve the objectives proposed in this work, a methodology with ten steps was developed. Four different workstations of an optical connector assembly company were selected to assess the stress levels of assemblers, using smart watch sensors, during their work activities. The workstations were evaluated for their physical load, through ergonomic analysis and for their cognitive load, through mental load classification. All data were statistically correlated. 
 
 Results and conclusion: Based on the statistical analysis of the data, it can be seen that the higher the level of complexity, the higher the average levels of stress presented. There is a direct influence on the level of stress and activities with greater complexity and related to physical (ergonomic) demands. The level of demand for carrying out activities, motivated by skills, rules and knowledge, mainly because they are related to decision-making, is related to the stress levels of employees, and can thus directly affect their mental health.
 
 Research implications: Through the results presented, we realized that reducing the demands of decision and reasoning during the activities requested at work, the company will be able to promote a reduction of the possible levels of stress of its employees, if this action does not impact on the reduction of their motivation for work. 
 
 Originality/Value: The method presented in this article differs from the others by comparing the different physical and mental demands with the stress levels measured with the use of smart watches during the activities of assembly line workers. It can be used to evaluate the effectiveness of an activity alternation approach, such as rotation, which can have a positive impact not only to minimize physical overloads but also to reduce levels of mental stress at work.

Experiment and numerical simulation of stress detection for oil and gas pipelines based on magnetic stress coupling of pipeline steel

In this study, a stress detection method is proposed to address the challenge of developing a nondestructive stress testing technique for oil and gas pipelines in real-time and online due to the complexity of the service environment and particularity of the transportation medium. The method is based on the magnetic mechanical properties of pipeline steel materials and the magnetic induction intensity stress coupling relationship. To evaluate the proposed method, six commonly used pipeline steels (X52, X56, X60, X65, X70, and X80) were selected as research objects, and their hysteresis curves were tested. The local magnetization key parameters of oil and gas pipelines were determined based on their magnetic properties. In addition, a finite element simulation model was established for oil and gas pipelines with coupling of magnetic field and stress field. This study appraise the distribution of magnetic field and magnetic induction intensity along the length and cross section height of the oil and gas pipelines simulation model after local magnetization, without being pressurized. Moreover, it examines the mechanism of magnetic induction intensity and stress variation of the simulation model under different internal pressures of the oil and gas pipelines. To this end, a coupling relationship of axial stress, circumferential stress, and magnetic induction intensity was separately established. Moreover, stress–strain test and magnetic induction intensity test systems were integrated to test the X80 pipeline steel. Through analyzing the test data and finite element simulation data, the validity of magnetic coupling simulation of oil and gas pipelines and the reliability of stress detection were verified. The results provide a theoretical basis for the key technologies of nondestructive online stress detection of oil and gas pipelines.

Semi-supervised approach for tweet-level stress detection

Psychological stress detection through social media has caught the interest of researchers due to the emergence of social media platforms like Twitter as major data sources to solicit human emotions and interactions. The problem of tweet-level stress detection is solved using various supervised learning mechanisms described in the existing literature. Given the real-world scenario in which there is a scarcity of labeled data and an abundance of unlabeled data, the semi-supervised solutions form an ideal choice. In this work, a semi-supervised approach called “Self-training Method for Tweet-level Stress Detection (SMTSD)” is proposed to extend the self-training approach for utilizing the information from sarcasm in predicting the pseudo-labels. In SMTSD, logistic regression is used as the base classifier. The predicted pseudo-labels are then used to train and build supervised baseline models on all the datasets collected using Twitter’s Tweepy API. The proposed solution of SMTSD, with the usage of sarcasm in predicting the pseudo-label, surpasses the performance of the basic self-training approach. Moreover, the proposed model also outperforms the supervised models considered and also gives better performance than the state-of-the-art techniques like Bi-LSTM. It is demonstrated that as the sample size for combined data of tweets with predicted pseudo-labels increases, so does the performance of the supervised models trained on them.

Is your mouse attracted by your eyes: Non-intrusive stress detection in off-the-shelf desktop environments

Increasing number of people work long hours with computers under high cognitive load. This could potentially cause mental stress in workplaces. Prolonged exposure to mental stress contributes to poor working experience and even severe health problems. Despite the growing demand, the existing intelligent stress detection methods are limited when applied to actual workplaces. They often measure physiological and physical signals, via intrusive devices, to detect stress. The intrusiveness hampers their accessibility and applicability in daily life and workplaces. To overcome that, behavior-based methods were proposed. Models that explore mouse and gaze behaviors during computer usages were demonstrated to be particularly effective. However, the current methods rely on using prior knowledge of the user interface (UI) layout to construct models. Their applicability thus is limited, especially in real workplaces where task UI is often dynamic. This paper presents a novel stress detection method to address the challenges. It attains non-intrusiveness and UI-agnostic by modeling the relative movement and coordination of mouse and gaze. The method is evaluated on a dynamic-UI task, namely, web searching. An accuracy of 78.8% is achieved using a commercial eye-tracker for gaze estimation, beating the state-of-the-art approaches by around 20%. We further use webcam to estimate gaze locations substituting for the eye-tracker, to enhance the model accessibility. The method yields 68.6% accuracy of stress detection without using any special devices. Experimental results demonstrate the effectiveness and applicability of our method. It opens up a new avenue for cognitive-aware adaptive user interface, intelligent working environment, and related applications.

Working Stress Measurement of Prestressed Rebars Using the Magnetic Resonance Method

Prestressed rebars are usually used to apply vertical prestress to concrete to prevent web cracking. The reduction of working stress will affect the durability of the structure. However, the existing working stress detection methods for prestressed rebars still need to be improved. To monitor the working stress of rebars, a magnetic resonance sensor was introduced to carry out experimental research. The correlation between rebar stress and the sensor’s induced voltage was theoretically analyzed using the magnetoelastic effect and magnetic resonance theory. A working stress monitoring method for prestressed rebars based on magnetic resonance was proposed. Working stress monitoring experiments were carried out for 16 mm, 18 mm, and 20 mm diameter rebars. The results showed that the induced voltage peak-to-peak value and the rebar prestress were nonlinearly correlated under different working conditions. Correlations between the characteristic indicators and the rebar working stress were obtained using nonlinear and linear fit. The cubic polynomial segmented fit outperformed the gradient overall linear fit, with the goodness of fit R2 greater than 0.96. The average relative error values of working stress monitoring were less than 5% under different working conditions. This provides a new method for working stress measurement of vertical prestressed rebars.

Stress Detection Using Machine Learning

Stress management systems are essential to identify and address stress levels that can disrupt our socioeconomic functioning. According to the World Health Organization (WHO), one in four people experience stress, which can result in mental and socioeconomic issues, poor work relationships, and depression, and in severe cases, suicide. Counselling is crucial to help people cope with stress, and while stress cannot be avoided, preventive measures can be taken to mitigate its effects. Currently, only medical and physiological experts can determine whether someone is experiencing stress. Traditional stress detection methods rely on self-reported answers, which can be unreliable. Automated stress detection can minimize health risks and improve societal welfare. Therefore, there is a need for a scientific tool that can automate stress detection using physiological signals. Stress detection is an important social contribution that potential to improve quality of life. As IT industries bring new technologies and products to the market, stress levels in employees are also increasing. While some organizations offer mental health services to their employees, more needs to be done to address this issue. Keywords: Machine Learning, Stress Detection, Haar cascade algorithm, Convolutional Neural Network

Improved Spectral Detection of Nitrogen Deficiency and Yellow Mosaic Disease Stresses in Wheat Using a Soil Effect Removal Algorithm and Machine Learning

Wheat yellow mosaic disease is a low-temperature and soil-borne disease. Crop infection by the yellow mosaic virus can lead to severe yield and economic losses. It is easily confused with nitrogen deficiency based on the plant’s morphological characteristics. Timely disease detection and crop management in the field require the precise identification of crop stress types. However, the detection of crop stress is often underappreciated. Wheat nitrogen deficiency and yellow mosaic disease were investigated in the field and wheat physiological and biochemical experiments were conducted to collect agronomic indicators, four years of reflectance spectral data at green-up and jointing were collected, and then studies for the detection of nitrogen deficiency and yellow mosaic disease stresses were carried out. The continuous removal (CR), first-order derivative (FD), standard normal variate (SNV), and spectral separation of soil and vegetation (3SV) preprocessing methods and 96 spectral indices were evaluated. The threshold method and variance inflation factor (TVIF) were used as feature selection methods combined with machine learning to develop a crop stress detection method. The results show that the most sensitive wavelengths are found in the 725–1000 nm region, while the sensitivity of the spectrum in the 400–725 nm region is lower. The PRI670,570, B, and RARSa spectral indices can detect nitrogen deficiency and yellow leaf disease stress, and the OA and Kappa values are 93.87% and 0.873, respectively, for PRI670,570, which is the best index. A 3SV-TVIF-SVM stress detection method was then proposed, using OA and Kappa values of 96.97% and 0.931, respectively, for field data validation. The results of the study can provide technical support and a theoretical basis for the accurate control of yellow mosaic disease and nitrogen fertilizer management in the field.

Chatbot in Artificial Intelligence

Abstract: This paper discloses a virtual conversational method and system to relieve the psychological stress of adolescents. It also aims at providing some positive information through continuous dialogue answers in order to guide adolescents to think and face difficulties with a positive and optimistic attitude and the agenda of reliving the psychological pressure of the adolescents can be achieved. Conventional face-to-face stress detection and relief methods do not work when confronted with those adolescents who are reluctant to express their negative emotions to the people in real life. In this paper, we would like to present an adolescent- oriented intelligent conversational chatting system called “HappySoul”, which acts as a virtual friend who can assist to encourage, understand, comfort, and guide stressful adolescents to pour out their bad and negative feelings, thereby releasing the stress. Chatbots, or conversational interfaces, present a new way for adolescents to interact with computer systems. This chatbot will allow a user to simply ask questions in the same way that they would address a human. The technology at the core of the proposed chatbot is natural language processing (“NLP”), RNN and client server architecture with the help of Android GUI.

Spectral Splitting Sensing Using Optical Fiber Bragg Grating for Spacecraft Lateral Stress Health Monitoring

Optical fiber sensing is a promising detection method for spacecraft health monitoring, since optical fiber sensors are lightweight, small in size, easy to integrate and immune to electromagnetic interference. As a significant optical sensor, fiber Bragg gratings (FBG) are widely used for force sensing because of their axial strain characteristics. However, it is necessary to detect not only one-dimensional strain but also plane strain and its deformation in order to comprehensively evaluate the condition of the structure. Therefore, it is very important to analyze the reflection spectrum of FBG under lateral stress. When FBG are subjected to lateral stress, the refractive index of the waveguide in the x and y directions changes, resulting in a birefringence phenomenon. This result causes the reflection spectrum of FBG to split into two peaks. In this paper, a transverse stress detection method based on spectral split sensing for the fiber Bragg grating is proposed, intended for monitoring spacecraft–small particle collisions. The FBG local lateral stress detection system is designed and verified by experiments. The wavelength pressure correlation is established in the experiment by adjusting the number of weights to change the lateral pressure on the FBG. The loading range of FBG lateral pressure is 4.0–7.0 N, the step size is 0.5 N, and round-trip measurement is carried out four times. The wavelengths of the peak and split point of the FBG reflection spectrum are recorded. The experimental results show that FBG’s split point and right peak pressure sensitivities are 16.57 pm/N and 45.14 pm/N, respectively. The spectral splitting phenomenon can be applied in spacecraft structure health monitoring systems and has certain reference value for the simplification of sensor systems.

Surface residual stress detection method for aero-engine blades based on active thermal deformation excitation

ABSTRACT Surface residual stress is made on the aero-engine blades by shot peening to improve the fatigue life of the blades. As the service time increases, the surface residual stress is decreasing due to thermomechanical relaxation and the centrifugal load caused by high-speed rotation. Therefore, the surface residual stress detection is of great importance for predicting the remaining life of aero-engine blades. Up to now, efficient and non-contact detection of surface residual stress is still a challenge. In this paper, an active thermal deformation excitation (ATDE) method for surface residual stress is proposed. According to the thermal deformation theory, the thermal deformation of the material is positively correlated with the surface residual stress, and by measuring the thermal deformation the surface residual stress can be detected and evaluated. The theoretical deformation model was established, and simulation models with different surface residual stresses were constructed. A detection system was proposed to detect the blade deformation with different surface residual stresses. Results show that there was a linear relationship between the blade deformation and the surface residual stress from −636 Mpa to −1403 Mpa.