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Journal articles on the topic 'Stress detection and management'
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Shrawankar, Urmila, and Chaitali Chandankhede. "Sarcasm Detection for Workplace Stress Management." International Journal of Synthetic Emotions 10, no. 2 (July 2019): 1–17. http://dx.doi.org/10.4018/ijse.2019070101.
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Working stress is becoming very common. Handling working stress at the workplace is really going to be challenging. As a result, most of the time most of the time people start behaving in sarcastic ways through verbal communication, through different gestures, using emoticons, or writing reviews or comments that leads to increasing their anxiety sometimes promotes depression. It is difficult to identify sarcasm in written notes or communication. Feedback analysis is not a direct method since feedback or employer reviews are written in more formal language. This motivates the authors to work on the employee feedback system. The currently developed system helps to detect the sarcastic emotions by applying different methodologies on several types of statements. This will help corporations and other big organizations to identify reasons behind sarcastic behavior or increased anxiety. As a result, they can promote counseling programs, psychological treatment, or yoga-meditation camps.
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G, Divya Shree. "Stress-Level Detection Using RepVGG Neural Network." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 31, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem35132.
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Stress used to impact our mental and physical well-being,productivity,and overall quality of life. Detecting stress accurately is vital for timely intervention and effective management. In this study, we introduce a new method for detecting stress levels using the RepVGG deep learning architecture. RepVGG stands out for its efficient performance and straightforward structure, making it ideal for analyzing physiological signals and other stress indicators. We using standard metrics to calculate the things like accuracy, precision, recall and etc. Ourfindings reveal that the RepVGG-based method excels in detecting stress levels, surpassing many traditional methods and other deep learning models. Moreover, the model shows strong generalization capabilities across various datasets and conditions. This research underscores the potential of advanced deep learning models like RepVGG in stress detection, opening doors for real-time, scalable, and precise stress monitoring systems. Looking ahead, weaim to integrate this model into wearable devices andmobile apps, enabling continuous stress monitoring and offering personalized stress management advice. Some approach utilizes a rich dataset consists of signals such (HRV) and (GSR), along with other relevant biomarkers. To ensure the model's robustness, we preprocess and augment this data. We then train the RepVGG architecture on this dataset, harnessing its Neural layers for feature extraction and its unique re-parameterizable design for efficient use.
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Zhou, Quan, Jinjia Kuang, Linfeng Yu, Xudong Zhang, Lili Ren, and Youqing Luo. "Discriminating between Biotic and Abiotic Stress in Poplar Forests Using Hyperspectral and LiDAR Data." Remote Sensing 16, no. 19 (October 9, 2024): 3751. http://dx.doi.org/10.3390/rs16193751.
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Sustainable forest management faces challenges from various biotic and abiotic stresses. The Asian longhorned beetle (ALB) and drought stress both induce water shortages in poplar trees, but require different management strategies. In northwestern China, ALB and drought stress caused massive mortality in poplar shelterbelts, which seriously affected the ecological functions of poplars. Developing a large-scale detection method for discriminating them is crucial for applying targeted management. This study integrated UAV-hyperspectral and LiDAR data to distinguish between ALB and drought stress in poplars of China’s Three-North Shelterbelt. These data were analyzed using a Partial Least Squares-Support Vector Machine (PLS-SVM). The results showed that the LiDAR metric (elev_sqrt_mean_sq) was key in detecting drought, while the hyperspectral band (R970) was key in ALB detection, underscoring the necessity of integrating both sensors. Detection of ALB in poplars improved when the poplars were well watered. The classification accuracy was 94.85% for distinguishing well-watered from water-deficient trees, and 80.81% for detecting ALB damage. Overall classification accuracy was 78.79% when classifying four stress types: healthy, only ALB affected, only drought affected, and combined stress of ALB and drought. The results demonstrate the effectiveness of UAV-hyperspectral and LiDAR data in distinguishing ALB and drought stress in poplar forests, which contribute to apply targeted treatments based on the specific stress in poplars in northwest China.
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Hanchate, Rohini, Harshal Narute, and Siddharam Shavage. "Stress Detection Using Machine Learning." International Journal of Science and Healthcare Research 8, no. 2 (May 25, 2023): 307–11. http://dx.doi.org/10.52403/ijshr.20230239.
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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
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Ferreira, Simão, Matilde A. Rodrigues, and Nuno Rocha. "P-434 NEXT-GEN STRESS MANAGEMENT: AI’S REVOLUTIONARY ROLE IN WORKPLACE OCCUPATIONAL HEALTH." Occupational Medicine 74, Supplement_1 (July 1, 2024): 0. http://dx.doi.org/10.1093/occmed/kqae023.1148.
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Abstract Introduction Occupational stress accounts for nearly half of all lost workdays, underscoring the urgent need for real-time, practical solutions. To address this, we introduced a non-intrusive, multimodal approach for detecting work-related stress, leveraging videoplethysmography and self-reported metrics to capture a comprehensive picture of stress dynamics in real-life settings. Methods In our study involving 28 participants over three months, we collected physiological data during their regular 8-hour workdays, supplemented by self-reported measures collected through a specially designed app. This integrative method facilitated the labeling of physiological data with perceived stress levels, subsequently aiding in the development of an accurate stress detection model. Results The refined model exhibits an accuracy that exceeds 90% and an F1 score above 92%, highlighting its potential in fostering a healthier work environment. Discussion Moving forward, we aim to couple this detection model with a personalized digital coaching module, focusing on user-centric recommendations. This innovative strategy, grounded in user preferences, seeks not only to alert individuals to immediate stress peaks but also offers tailored suggestions for quick stress relief, thereby nurturing sustained engagement and adherence. Conclusion Our initiative showcases the feasibility of ethical, unobtrusive stress monitoring, promising a pathway to a healthier workforce by averting potential stress episodes and reducing chronic stress conditions. Future efforts will concentrate on assessing the positive impacts and efficacy of implementing such a recommender system.
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KONNI, SRIKANTH REDDY, and KHAJA ZIAUDDIN. "UTILIZING MACHINE LEARNING AND IMAGE PROCESSING TO DETECT SIGNS OF STRESS IN INDIVIDUALS." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 3 (December 15, 2020): 2838–48. http://dx.doi.org/10.61841/turcomat.v11i3.14588.
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This study's main objective is to identify stress in the human body using vivid machine learning and image processing techniques. Our system is an improved version of earlier stress detection systems that lacked personal counseling or live detection. Instead, it detects employees' levels of both physical and mental stress and offers appropriate stress management strategies through a survey form. Additionally, our system includes periodic analysis of employees and live detection. To make the most of employees during working hours, our approach is mainly concerned with stress management and fostering a positive, flexible work environment.
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Radhika R, Devika A, Sreeranjani S, and Dharshini KR. "A CHATBOT-BASED APPROACH FOR STRESS LEVEL DETECTION AND MANAGEMENT." International Journal of Trendy Research in Engineering and Technology 07, no. 05 (2023): 14–17. http://dx.doi.org/10.54473/ijtret.2023.7504.
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Jerath, Ravinder, Mohammad Syam, and Shajia Ahmed. "The Future of Stress Management: Integration of Smartwatches and HRV Technology." Sensors 23, no. 17 (August 22, 2023): 7314. http://dx.doi.org/10.3390/s23177314.
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In the modern world, stress has become a pervasive concern that affects individuals’ physical and mental well-being. To address this issue, many wearable devices have emerged as potential tools for stress detection and management by measuring heart rate, heart rate variability (HRV), and various metrics related to it. This literature review aims to provide a comprehensive analysis of existing research on HRV tracking and biofeedback using smartwatches pairing with reliable 3rd party mobile apps like Elite HRV, Welltory, and HRV4Training specifically designed for stress detection and management. We apply various algorithms and methodologies employed for HRV analysis and stress detection including time-domain, frequency-domain, and non-linear analysis techniques. Prominent smartwatches, such as Apple Watch, Garmin, Fitbit, Polar, and Samsung Galaxy Watch, are evaluated based on their HRV measurement accuracy, data quality, sensor technology, and integration with stress management features. We describe the efficacy of smartwatches in providing real-time stress feedback, personalized stress management interventions, and promoting overall well-being. To assist researchers, doctors, and developers with using smartwatch technology to address stress and promote holistic well-being, we discuss the data’s advantages and limitations, future developments, and the significance of user-centered design and personalized interventions.
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Solanke, Prof S. A., Shreyash S. Tidke, Tejas G. Malokar, Sarvesh S. Udapurkar, Faiz Mohammad Sheikh, and Prajakta G. Gaikwad. "Stress Detection System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (February 29, 2024): 1676–80. http://dx.doi.org/10.22214/ijraset.2024.58627.
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Abstract: Stress is a pervasive aspect of modern life, posing significant health risks if left unmanaged. Early detection of stress is crucial for preventing adverse health outcomes and promoting well-being. This paper presents a novel approach to stress monitoring and management using machine learning (ML) techniques and wearable physiological sensors. By analyzing multimodal datasets, including electrocardiogram (ECG) signals and other physiological parameters, our proposed model aims to accurately detect stress levels in individuals. Leveraging low-cost wearable sensors and IoT technology, our system provides real-time feedback and alerts individuals to their stress levels, enabling proactive intervention to mitigate health risks. Through a comprehensive review of existing stress detection approaches and integration of ML algorithms, our study contributes to the development of more efficient and effective stress monitoring systems. This research holds promise for improving health outcomes and enhancing quality of life in individuals facing stress-related challenges.
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Jambhale, Kiran, Smridhi Mahajan, Benjamin Rieland, Nilanjan Banerjee, Abhijit Dutt, Sai Praveen Kadiyala, and Ramana Vinjamuri. "Identifying Biomarkers for Accurate Detection of Stress." Sensors 22, no. 22 (November 11, 2022): 8703. http://dx.doi.org/10.3390/s22228703.
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Substance use disorder (SUD) is a dangerous epidemic that develops out of recurrent use of alcohol and/or drugs and has the capability to severely damage one’s brain and behaviour. Stress is an established risk factor in SUD’s development of addiction and in reinstating drug seeking. Despite this expanding epidemic and the potential for its grave consequences, there are limited options available for management and treatment, as well as pharmacotherapies and psychosocial treatments. To this end, there is a need for new and improved devices dedicated to the detection, management, and treatment of SUD. In this paper, the negative effects of SUD-related stress were discussed, and based on that, a few significant biomarkers were selected from a set of eight features collected by a chest-worn device, RespiBAN Professional, on fifteen individuals. We used three machine learning classifiers on these optimal biomarkers to detect stress. Based on the accuracies, the best biomarkers to detect stress and those considered as features for classification were determined to be electrodermal activity (EDA), body temperature, and a chest-worn accelerometer. Additionally, the differences between mental stress and physical stress, as well as different administrations of meditation during the study, were identified and analysed. Challenges, implications, and applications were also discussed. In the near future, we aim to replicate the proposed methods in individuals with SUD.
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Jyothi, Ms A. Aruna. "Stress Detection using Deep Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 4895–903. http://dx.doi.org/10.22214/ijraset.2023.54543.
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Abstract: Stress is a typical component of daily life that has an impact on people in different circumstances. However, sustained or acute stress can negatively impact our health and interfere with our daily activities. Early recognition of mental stress is essential for good management and the avoidance of future health problems brought on by chronic stress. Understanding the connection between facial expressions and the accompanying emotional experiences of individuals is a topic of great interest. According to research, facial expressions and indications might offer important clues for the study and classification of stress. Notably, changes in the mouth and eyebrows are important signs of stress on the human face. This technology records live video and applies conventional conversion to capture stress levels. In order to analyse the user's stress levels, this system records live video and uses conventional conversion and image processing techniques. The technology provides more precise and effective results in stress prediction by utilising machine learning algorithms that concentrate on brow and lip motions.
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S, Induja, Pavan Kumar G. R, Rita Mary, and Rama Dhananjaya V. "Stress Detection in IT Professionals Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 1498–503. http://dx.doi.org/10.22214/ijraset.2024.61865.
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Abstract: In today's fast-paced technology landscape, stress management. The work environment in the IT field is often characterized by long hrs, and high expectations, which can leading to elevated stress levels. Unchecked stress not only impacts the health and well-being of professionals but also affects in job satisfaction. This study aims to predicting the stress levels of IT professionals using machine learning techniques, thereby aiding in proactive stress management. We utilize a range of features indicative of work stress, including Heart Rate, Skin Conductivity, Hours Worked, Number of Emails Sent, and Meetings Attended. These features provide a physiological and work-related factors that contribute to stress. The application of [ML] in this context serves as an innovative approach to an increasingly pertinent issue. By leveraging the power of data analytics, this model aims to organizations. Individuals we can use for self-monitoring and early intervention, while organizations can utilize them to identify high-stress environments or roles, thereby allocating resources or interventions more effectively. Our preliminary results indicate a strong correlation between the chosen features and stress levels, demonstrating the viability of using ml for stress prediction in IT professionals. This study stands as a crucial step towards a more data-driven approach to mental health condition
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Prachi S Ramteke, Et al. "Survey of Applications of Ml in Stress Detection." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (November 5, 2023): 4268–76. http://dx.doi.org/10.17762/ijritcc.v11i9.9882.
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Stress is a common and pervasive issue that affects millions of people worldwide. It can lead to a variety of negative health outcomes, including anxiety, depression, and physical health problems. Early detection of stress is crucial for effective management and prevention of these negative outcomes. Stress detection technologies using machine learning algorithms can provide individuals with valuable information about their stress levels and help them manage their stress in more effective ways. This can lead to improved mental and physical health outcomes, as well as increased productivity and overall well-being. Therefore, stress detection is an important area of research that has the potential to positively impact the lives of many people.
This paper presents a survey of techniques applicable to the field of stress detection using machine learning (ML) algorithms. We categorize these techniques based on the approach they take and discuss various challenges, open questions, and future work in this area. We present a taxonomy of existing research and finally discuss gaps and future directions of research to advance the study of stress management using most recent ML techniques. These technologies
provide individuals with valuable information about their stress levels and can help them manage their stress in more effective ways.
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Hickey, Blake Anthony, Taryn Chalmers, Phillip Newton, Chin-Teng Lin, David Sibbritt, Craig S. McLachlan, Roderick Clifton-Bligh, John Morley, and Sara Lal. "Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review." Sensors 21, no. 10 (May 16, 2021): 3461. http://dx.doi.org/10.3390/s21103461.
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Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.
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Roel P. Masongsong. "System for Biological Sensor-Based Stress Detection Using the Bohm-Jacopini Algorithm." Journal of Electrical Systems 20, no. 5s (April 13, 2024): 1224–31. http://dx.doi.org/10.52783/jes.2438.
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Chronic stress has become a pervasive issue in modern society, particularly impacting professions like car driving. While mitigating stress entirely may not be feasible, exploring effective coping mechanisms is crucial. Although research on stress management is extensive, few studies have harnessed the power of modern technology to assess stress levels and simultaneously develop integrated management solutions.
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Choi, Eun Bin, and Soo Hyung Kim. "Convolutional Autoencoder based Stress Detection using Soft Voting." Korean Institute of Smart Media 12, no. 11 (December 31, 2023): 9–17. http://dx.doi.org/10.30693/smj.2023.12.11.9.
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Stress is a significant issue in modern society, often triggered by external or internal factors that are difficult to manage. When high stress persists over a long term, it can develop into a chronic condition, negatively impacting health and overall well-being. However, it is challenging for individuals experiencing chronic stress to recognize their condition, making early detection and management crucial. Using biosignals measured from wearable devices to detect stress could lead to more effective management. However, there are two main problems with using biosignals: first, manually extracting features from these signals can introduce bias, and second, the performance of classification models can vary greatly depending on the subject of the experiment. This paper proposes a model that reduces bias using convo utional autoencoders, which can represent the key features of data, and enhances generalizability by employing soft voting, a method of ensemble learning, to minimize performance variability. To verify the generalization performance of the model, we evaluate it using LOSO cross-validation method. The model proposed in this paper has demonstrated superior accuracy compared to previous studies using the WESAD dataset.
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Praharshitha, Vishwagna, Dr Gifta Jerith G, and Dr Thayabba Katoon. "Stress Detection in IT Professionals using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 08 (August 6, 2024): 1–12. http://dx.doi.org/10.55041/ijsrem36965.
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In today's fast-paced technology landscape, stress management is becoming increasingly important, especially among IT professionals. The work environment in the IT industry is often characterized by long hours, tight deadlines, and high expectations, which can lead to elevated stress levels. Unchecked stress not only impacts the health and well-being of professionals but also affects productivity and job satisfaction. This study aims to predict the stress levels of IT professionals using machine learning techniques, thereby aiding in proactive stress management. We utilize a range of features indicative of work stress, including Heart Rate, Skin Conductivity, Hours Worked, Number of Emails Sent, and Meetings Attended. These features provide a comprehensive view of both the physiological and work-related factors that contribute to stress. The application of machine learning in this context serves as an innovative approach to an increasingly pertinent issue. By leveraging the power of data analytics, this model aims to provide actionable insights for both individuals and organizations. Individuals can use these predictions for self-monitoring and early intervention, while organizations can utilize them to identify high-stress environments or roles, thereby allocating resources or interventions more effectively. Our preliminary results indicate a strong correlation between the chosen features and stress levels, demonstrating the viability of using machine learning for stress prediction in IT professionals. This study stands as a crucial step towards a more data-driven approach to mental health and well-being in the workplace. Key Words: : Randomforest, adaboost, extratree, Stress Detection, IT Professionals, Machine Learning, Stress Analysis, Mental Health
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Thapliyal, Himanshu, Vladislav Khalus, and Carson Labrado. "Stress Detection and Management: A Survey of Wearable Smart Health Devices." IEEE Consumer Electronics Magazine 6, no. 4 (October 2017): 64–69. http://dx.doi.org/10.1109/mce.2017.2715578.
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Tartaglione, Girolamo, Giuseppe Visconti, Roberto Bartoletti, Stefano Gentileschi, Marzia Salgarello, Domenico Rubello, and Patrick M. Colletti. "Stress Lymphoscintigraphy for Early Detection and Management of Secondary Limb Lymphedema." Clinical Nuclear Medicine 43, no. 3 (March 2018): 155–61. http://dx.doi.org/10.1097/rlu.0000000000001963.
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Kalyan, Naralasetti Prudhvi. "Stress Detection in IT Employees using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (May 1, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem32772.
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In today's fast-paced technology landscape, stress management is becoming increasingly important, especially among IT professionals. The work environment in the IT industry is often characterized by long hours, tight deadlines, and high expectations, which can lead to elevated stress levels. Unchecked stress not only impacts the health and well-being of professionals but also affects productivity and job satisfaction. This study aims to predict the stress levels of IT professionals using machine learning techniques, thereby aiding in proactive stress management. We utilize a range of features indicative of work stress, including Heart Rate, Skin Conductivity, Hours Worked, Number of Emails Sent, and Meetings Attended. These features provide a comprehensive view of both the physiological and work-related factors that contribute to stress. The application of machine learning in this context serves as an innovative approach to an increasingly pertinent issue. By leveraging the power of data analytics, this model aims to provide actionable insights for both individuals and organizations. Individuals can use these predictions for self-monitoring and early intervention, while organizations can utilize them to identify high-stress environments or roles, thereby allocating resources or interventions more effectively. Our preliminary results indicate a strong correlation between the chosen features and stress levels, demonstrating the viability of using machine learning for stress prediction in IT professionals. This study stands as a crucial step towards a more data-driven approach to mental health and well-being in the workplace Key Words: Random Forest , AdaBoost Classifier, Extra Tree Classifier, Decision Tree, Stacking etc.
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Guerbaoui, Mohammed, Ismail Ichou, Zakaria Bakziz, Abdelouahed Selmani, Samira El Faiz, Abdelali Ed-Dahhak, Bachir Benhala, and Abdeslam Lachhab. "Detection and Management of Water Stress at Plants by Deep Learning and Image processing Case-study of Tomato." E3S Web of Conferences 601 (2025): 00007. https://doi.org/10.1051/e3sconf/202560100007.
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This project aims to develop an innovative technique for detecting water stress in tomato plants using deep learning and image processing techniques, and to integrate it into a mobile application for real-time monitoring. The methodology adopted includes the acquisition and preprocessing of image data, the construction and training of a deep learning model, and the development of a user-friendly mobile application. The results show a promising performance of the model in the precise detection of water stress, confirming the usefulness and usability of the developed mobile application.
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Namkung, Junghyun, Seok Min Kim, Won Ik Cho, So Young Yoo, Beomjun Min, Sang Yool Lee, Ji-Hye Lee, et al. "Novel Deep Learning-Based Vocal Biomarkers for Stress Detection in Koreans." Psychiatry Investigation 21, no. 11 (November 25, 2024): 1228–37. http://dx.doi.org/10.30773/pi.2024.0131.
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Objective The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions.Methods We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores.Results The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks.Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.
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Ankita Gandhi. "Stress Detection through EEG Signals: Employing a Hybrid Approach integrating Time Domain, Frequency Domain Features and Machine Learning Techniques." Journal of Electrical Systems 20, no. 3 (April 25, 2024): 3965–73. http://dx.doi.org/10.52783/jes.5402.
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Mental stress is a widespread problem that affects people all over the world and can have negative health consequences if not appropriately controlled. The importance of early detection and management in minimizing the detrimental effects of stress cannot be overstated. This study provides a hybrid technique for stress detection that combines time domain and frequency domain information taken from EEG data. Machine learning techniques are used to create a stress detection model that is accurate and dependable. The goal is to increase the accuracy and reliability of stress detection so that prompt intervention and assistance may be provided. The paper opens with an introduction to stress, its effects on mental health, and the need for automated stress detection systems. EEG signals are introduced as a valuable data source for recording stress-related brain activity. To test the model's success in stress detection, performance evaluation criteria such as accuracy, sensitivity, specificity, and F1-score are used. When the result compared to the present approach, the Hybrid Approach has higher accuracy (SVM-98.33% & RF-95%).
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HIKOSAKA, Shoko. "Water Stress Detection and Irrigation Management Techniques for High-Quality Tomato Production." Shokubutsu Kankyo Kogaku 34, no. 3 (2022): 129–35. http://dx.doi.org/10.2525/shita.34.129.
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Tartaglione, Girolamo, Giuseppe Visconti, Roberto Bartoletti, Stefano Gentileschi, Francesco Pio Ieria, Patrick M. Colletti, Domenico Rubello, and Marzia Salgarello. "Intradermal-Stress-Lymphoscintigraphy in Early Detection and Clinical Management of Secondary Lymphedema." Clinical Nuclear Medicine 44, no. 8 (August 2019): 669–73. http://dx.doi.org/10.1097/rlu.0000000000002560.
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Kiranashree, B. K., V. Ambika, and A. D. Radhika. "Analysis on Machine Learning Techniques for Stress Detection among Employees." Asian Journal of Computer Science and Technology 10, no. 1 (May 5, 2021): 35–37. http://dx.doi.org/10.51983/ajcst-2021.10.1.2698.
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Mental stress is a common and major issue nowadays especially among working professional, because employees have family commitments with their over workload, target, achievements, etc. Stress tends various health issues like heart attack, stroke, depression, and suicide. Mental stress is not only in employees even normal people also face this problem but the employees has so many stress management techniques to manage the stress like yoga, meditation etc., but still employees suffer from the stress. Stress calculated by the Traditional stress detection method has two types of physiological parameters one is questionnaire format and another one is physiological signals based on Heart rate variability, galvanic skin response, BP, and electrocardiography, etc., Machine learning techniques are applied to analyze and anticipate stress in employees. In this paper, we mainly focus on different machine learning techniques and physiological parameters for stress detection.
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Alghamdi, Zeyad, Tharindu Kumarage, Mansooreh Karami, Faisal Alatawi, Ahmadreza Mosallanezhad, and Huan Liu. "Studying the Influence of Toxicity and Emotion Features for Stress Detection on Social Media." European Conference on Social Media 10, no. 1 (May 5, 2023): 42–51. http://dx.doi.org/10.34190/ecsm.10.1.1028.
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It is crucial to detect and manage stress as early as possible before it becomes a severe mental and physical health problem. Some authors even introduce stress as a “silent killer” to emphasize the significance of early stress management. Traumatic global events such as COVID-19 have amplified stress throughout online communities and it is quite common to see that social media users often vent about their problems or situations online. The ability to detect a person's stress from their posts on social media platforms like Reddit or Twitter in a timely manner can help early stress management and consequently counters mental health conditions. In order to detect stress from social media posts, we must obtain the characteristics that signal a user's stress. Which motivates us to study how salient features influence stress detection. On social media, text-based methods of communication predominantly overtake verbal forms, which makes these platforms a convenient rich medium with an extensive amount of text content to analyze a user's thoughts and emotions. We present a novel approach that helps improve stress detection on social media textual content with sentiment, emotion, and toxicity features. We design our framework based on multiple Transformer-based state-of-the-art sentiment, emotion, and toxicity analysis tools and models for feature extraction and discuss the stress detection tasks’ interpretability via inspecting multiple dimensions. For the evaluation, we use publicly available and high-quality datasets where the social media posts are real, carefully selected and labeled. Our experiments show the influence of the proposed new feature dimensions on stress detection by comparing the state-of-the-art baselines and suggesting future directions in stress detection on social media. Furthermore, our extensive feature correlation analysis highlights different aspects, such as 1) Positive and Negative sentiment, 2) Joy, Sadness, and Fear emotions, and 3) Obscene and Insult toxicity as governing factors in improving stress detection performance.
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Patching, Alan, and Rick Best. "An Investigation into Psychological Stress Detection and Management in Organisations Operating in Project and Construction Management." Procedia - Social and Behavioral Sciences 119 (March 2014): 682–91. http://dx.doi.org/10.1016/j.sbspro.2014.03.076.
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Ladakis, I., and I. Chouvarda. "Overview of Biosignal Analysis Methods for the Assessment of Stress." Emerging Science Journal 5, no. 2 (April 1, 2021): 233–44. http://dx.doi.org/10.28991/esj-2021-01267.
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Objectives: Stress is a normal reaction of the human organism induced in situations that demand a level of activation. This reaction has both positive and negative impact on the life of each individual. Thus, the problem of stress management is vital for the maintenance of a person’s psychological balance. This paper aims at the brief presentation of stress definition and various factors that can lead to augmented stress levels. Moreover, a brief synopsis of biosignals that are used for the detection and categorization of stress and their analysis is presented. Methods: Several studies, articles and reviews were included after literature research. The main questions of the research were: the most important and widely used physiological signals for stress detection/assessment, the analysis methods for their manipulation and the implementation of signal analysis for stress detection/assessment in various developed systems. Findings: The main conclusion is that current researching approaches lead to more sophisticated methods of analysis and more accurate systems of stress detection and assessment. However, the lack of a concrete framework towards stress detection and assessment remains a great challenge for the research community. Doi: 10.28991/esj-2021-01267 Full Text: PDF
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Matson, Liana M., Amy B. Adler, Phillip J. Quartana, Connie L. Thomas, and Emily G. Lowery-Gionta. "Management of Acute Stress Reactions in the Military: A Stepped Care Approach." Current Psychiatry Reports 24, no. 12 (December 2022): 799–808. http://dx.doi.org/10.1007/s11920-022-01388-3.
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Abstract Purpose of the Review This review highlights knowledge gaps surrounding the development and use of interventions for Acute Stress Reactions (ASRs). First, we propose that a stepped care approach to intervention for ASR be developed and utilized in military operational environments. A stepped care approach would include detection and assessment, followed by behavioral intervention, and then medication intervention for ASRs. Second, we discuss potential strategies that can be taken for the development of safe and effective ASR medications. Recent Findings ASRs commonly occur in operational environments, particularly in military populations. ASRs impact the safety and performance of individual service members and teams, but there are currently limited options for intervention. Summary Efforts to improve ASR detection and assessment, and development and delivery of ASR interventions for implementation in operational environments, will be critical to maintaining the safety and performance of service members.
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Rachana K, Santosh S. Patil, and Venkatesh Polepalli. "Text Analysis with NLP and Machine Learning: A Multi-Domain Approach to Spam, Sentiment, Stress, and Hate Detection." International Journal of All Research Education and Scientific Methods 12, no. 12 (2024): 4242–47. https://doi.org/10.56025/ijaresm.2024.1212244242.
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Text analysis is a process of interpreting large volumes of textual data, with applications in content moderation, sentiment tracking, mental health, and social media management. This research focuses on four types of text analysis—spam detection, sentiment analysis, stress detection, and hate speech detection through techniques used in Natural Language Processing (NLP) techniques and different machine learning models. Spam and sentiment detection uses Naive Bayes for efficient text-based classification; stress detection uses logistic regression to identify stress-indicative patterns, and hate speech detection uses a Random Forest classifier for capturing complex harmful language patterns. Datasets for these tasks are taken from the UC Irvine Machine Learning Repository and Kaggle. This research shows the power of combining NLP and machine learning to improve classification accuracy, automate tasks, and handle ambiguity.
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Cvetković, Nikola, Aleksandar Đoković, Milan Dobrota, and Milan Radojičić. "New Methodology for Corn Stress Detection Using Remote Sensing and Vegetation Indices." Sustainability 15, no. 6 (March 21, 2023): 5487. http://dx.doi.org/10.3390/su15065487.
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Since corn is the second most widespread crop globally and its production has an impact on all industries, from animal husbandry to sweeteners, modern agriculture meets the task of preserving yield quality and detecting corn stress. Application of remote sensing techniques enabled more efficient crop monitoring due to the ability to cover large areas and perform non-destructive and non-invasive measurements. By using vegetation indices, it is possible to effectively measure the status of surface vegetation and detect stress on the field. This study describes the methodology for corn stress detection using red-green-blue (RGB) imagery and vegetation indices. Using the Excess Green vegetation index and calculated vegetation index histogram for healthy crop, corn stress has been effectively detected. The obtained results showed higher than 89% accuracy on both experimental plots, confirming that the proposed methodology can be used for corn stress detection using images acquired only with the RGB sensor. The proposed method does not depend on the sensor used for image acquisition and vegetation index used for stress detection, so it can be used in various different setups.
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Lazić, Olivera, Sandra Cvejić, Boško Dedić, Aleksandar Kupusinac, Siniša Jocić, and Dragana Miladinović. "Transfer Learning in Multimodal Sunflower Drought Stress Detection." Applied Sciences 14, no. 14 (July 10, 2024): 6034. http://dx.doi.org/10.3390/app14146034.
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Efficient water supply and timely detection of drought stress in crops to increase yields is an important task considering that agriculture is the primary consumer of water globally. This is particularly significant for plants such as sunflowers, which are an important source of quality edible oils, essential for human nutrition. Traditional detection methods are labor-intensive, time-consuming, and rely on advanced sensor technologies. We introduce an innovative approach based on neural networks and transfer learning for drought stress detection using a novel dataset including 209 non-invasive rhizotron images and 385 images of manually cleaned sections of sunflowers, subjected to normal watering or water stress. We used five neural network models: VGG16, VGG19, InceptionV3, DenseNet, and MobileNet, pre-trained on the ImageNet dataset, whose performance was compared to select the most efficient architecture. Accordingly, the most efficient model, MobileNet, was further refined using different data augmentation mechanisms. The introduction of targeted data augmentation and the use of grayscale images proved to be effective, demonstrating improved results, with an F1 score and an accuracy of 0.95. This approach encourages advances in water stress detection, highlighting the value of artificial intelligence in improving crop health monitoring and management for more resilient agricultural practices.
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Androutsou, Thelma, Spyridon Angelopoulos, Evangelos Hristoforou, George K. Matsopoulos, and Dimitrios D. Koutsouris. "A Multisensor System Embedded in a Computer Mouse for Occupational Stress Detection." Biosensors 13, no. 1 (December 22, 2022): 10. http://dx.doi.org/10.3390/bios13010010.
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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.
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AMAlANATHAN, SELVIA AM, ABDULAZIZ ASIRI, and AMER AL ALI. "Mental Health Prediction Using Artificial Intelligence- Machine Learning: Pain and Stress Detection Using Wearable Sensors and Devices—A Review." YMER Digital 21, no. 08 (August 12, 2022): 528–42. http://dx.doi.org/10.37896/ymer21.08/45.
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Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This re- view presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors. Various physiological signals (i.e., heart activity, brain activity, muscle activity, electrodermal activity, respiratory, blood volume pulse, skin tempera- ture) and behavioral signals are organized for wearables sensors detection. By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection. Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or devices. Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue. Keywords: Mental health, machine learning, pain detection; stress detection; wearable sensor; physiological signals; behavioral signals
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Wang, Pinhao, Lening Huang, Guang Dai, Jing Li, Jun Hu, Emilia Barakova, Cheng Yao, and Fangtian Ying. "Evaluating the Role of Interactive Encouragement Prompts for Parents in Parent–Child Stress Management." Applied Sciences 15, no. 1 (December 30, 2024): 256. https://doi.org/10.3390/app15010256.
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Parental involvement is crucial for children’s stress management, and co-regulation of stress can have a positive effect. To facilitate parental involvement in children’s stress management in learning, we proposed an embodied connected system, which provides stress detection, stress information feedback, and encouragement prompts, aiming to help parents better understand and engage in children’s stress-regulation process. This article focuses on the impact of interactive encouragement prompts provided to parents on children’s stress management. The within-group experiment was used to collect stress data and scales from 36 parent–child groups during a controlled learning experiment, and semi-structured interviews were conducted with parents and children. The results indicate that the encouragement prompts provided to the parents enhance the effectiveness of stress relief in children facilitated by parental involvement. In particular, the psychological stress was reduced, and the communication between parents and children became more effective. In addition, active parental involvement and timely encouragement prompts can improve children’s stress-coping abilities, providing an interactive intervention approach for learning stress management.
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Sandeep, Nidubrolu Lakshmi. "HUMAN STRESS DETECTION BASED ON SLEEPING HABITS THROUGH MACHINE LEARNING ALGORITHMS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 25, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem31628.
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Stress is a mental or emotional state brought on by demanding or unavoidable circumstances, also referred to as stressors. In order to prevent any unfavorable occurrences in life, it is crucial to understand human stress levels. Sleep disturbances are related to a number of physical, mental, and social problems. This study's main objective is to investigate how human stress might be detected using machine learning algorithms based on sleep-related behaviors. Detecting and understanding stress patterns during sleep can provide valuable insights into individuals' overall stress levels and aid in the development of targeted interventions for stress management. This project focuses on the detection of human stress in and through sleep using various sensing modalities and machine learning techniques. The project employs a multimodal approach, combining physiological signals, sleep-related data, and contextual information to capture comprehensive stress patterns during sleep. Physiological signals such as heart rate, electro dermal activity, and respiratory patterns are collected using wearable sensors, while sleep-related data, including sleep stages and sleep quality metrics, are obtained through polysomnography and actigraphy. This data when given to the web application performs the prediction through the ML model and gives the predicted output. This project is made on a Decision Tree Classifier with best hyper parameters. Key Words: Stress, Sleep disturbances, Human stress, Physiological signals, Polysomnography, Actigraphy
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Calzone, Antonella, Lorenzo Cotrozzi, Giacomo Lorenzini, Cristina Nali, and Elisa Pellegrini. "Hyperspectral Detection and Monitoring of Salt Stress in Pomegranate Cultivars." Agronomy 11, no. 6 (May 22, 2021): 1038. http://dx.doi.org/10.3390/agronomy11061038.
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Advancements in the ability to detect plant responses to salinity are mandatory to improve crop yield, quality, and management practices. The present study shows the capability of hyperspectral reflectance (400–2400 nm) to rapidly and non-destructively detect and monitor the responses of two pomegranate cultivars (Parfianka, P, and Wonderful, W) under salt treatment (i.e., 200 mL of 100 mM NaCl solution every day) for 35 days. Analyzing spectral signatures from asymptomatic leaves, the two cultivars, as well as salinity conditions were discriminated. Furthermore, using a partial least squares regression approach, we constructed predictive models to concomitantly estimate (goodness-of-fit model, R2: 0.61–0.79; percentage of the root mean square error over the data range, %RMSE: 9–14) from spectra of various physiological leaf parameters commonly investigated in plant/salinity studies. The analyses of spectral signatures enabled the early detection of salt stress (i.e., from 14 days from the beginning of treatment, FBT), even in the absence of visible symptoms, but they did not allow the identification of the different degrees of salt tolerance between cultivars; this cultivar-specific tolerance to salt was instead reported by analyzing variations of leaf parameters estimated from spectra (W was less tolerant than P), which, in turn, allowed the detection of salt stress only at later times of analysis (i.e., slightly from 21 day FBT and, evidently, at the end of treatment). The proposed approach could be used in precision agriculture, high-throughput plant phenotyping, and smart nursery management to enhance crop quality and yield.
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Rabbani, Suha, and Naimul Khan. "Contrastive Self-Supervised Learning for Stress Detection from ECG Data." Bioengineering 9, no. 8 (August 8, 2022): 374. http://dx.doi.org/10.3390/bioengineering9080374.
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In recent literature, ECG-based stress assessment has become popular due to its proven correlation to stress and increased accessibility of ECG data through commodity hardware. However, most ECG-based stress assessment models use supervised learning, relying on manually-annotated data. Limited research is done in the area of self-supervised learning (SSL) approaches that leverage unlabelled data and none that utilize contrastive SSL. However, with the dominance of contrastive SSL in domains such as computer vision, it is essential to see if the same excellence in performance can be obtained on an ECG-based stress assessment dataset. In this paper, we propose a contrastive SSL model for stress assessment using ECG signals based on the SimCLR framework. We test our model on two ECG-based stress assessment datasets. We show that our proposed solution results in a 9% improvement in accuracy on the WESAD dataset and 3.7% on the RML dataset when compared with SOTA ECG-based SSL models for stress assessment. The development of more accurate stress assessment models, particularly those that employ non-invasive data such as ECG for assessment, leads to developments in wearable technology and the creation of better health monitoring applications in areas such as stress management and relaxation therapy.
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Maleeha Jeelani, Er. Yuvika. "Detection Of Stress And Its Levels Through Sleep Using AI." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (October 25, 2023): 1549–63. http://dx.doi.org/10.52783/tjjpt.v44.i4.1100.
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In this work, stress level detection during sleep using AI and physiological signals is explored. Stress and sleep, as well as their interplay in maintaining physical and mental health, are investigated. The potential of AI models in sleep monitoring and stress level detection is examined. Wearable technology is used to gather physiological signals such as breathing, heart rate, body temp, and others. To assure data quality, careful data preparation is carried out. and extraction of relevant features necessary for accurate stress level prediction. Two AI models, namely K-Nearest Neighbors (KNN) and Random Forest classifiers, are utilized for stress level prediction on a balanced dataset. To pinpoint the most important factors in stress level estimation, a feature significance analysis is also carried out. Remarkable accuracy is achieved by the AI models, with stress level prediction accuracy reaching approximately 99%. The potential of AI and physiological signals in revolutionizing stress assessment methodologies is highlighted. Broader implications of the findings, particularly the relationship between sleep and stress, are discussed, and ethical considerations, including data privacy and responsible AI implementation, are addressed. The integration of AI-based sleep analysis into wearable devices is envisioned, empowering individuals to manage stress and optimize their well-being. Valuable insights into stress level detection during sleep are contributed by this thesis, propelling forward the field of AI-driven healthcare applications. The groundwork for future advancements in stress management strategies and personalized healthcare solutions is laid by this research.
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Janni, Michela, Nicola Coppede, Manuele Bettelli, Nunzio Briglia, Angelo Petrozza, Stephan Summerer, Filippo Vurro, et al. "In Vivo Phenotyping for the Early Detection of Drought Stress in Tomato." Plant Phenomics 2019 (November 27, 2019): 1–10. http://dx.doi.org/10.34133/2019/6168209.
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Drought stress imposes a major constraint over a crop yield and can be expected to grow in importance if the climate change predicted comes about. Improved methods are needed to facilitate crop management via the prompt detection of the onset of stress. Here, we report the use of an in vivo OECT (organic electrochemical transistor) sensor, termed as bioristor, in the context of the drought response of the tomato plant. The device was integrated within the plant’s stem, thereby allowing for the continuous monitoring of the plant’s physiological status throughout its life cycle. Bioristor was able to detect changes of ion concentration in the sap upon drought, in particular, those dissolved and transported through the transpiration stream, thus efficiently detecting the occurrence of drought stress immediately after the priming of the defence responses. The bioristor’s acquired data were coupled with those obtained in a high-throughput phenotyping platform revealing the extreme complementarity of these methods to investigate the mechanisms triggered by the plant during the drought stress event.
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Marino, Stefano. "Horticultural Crop Response to Different Environmental and Nutritional Stress." Horticulturae 7, no. 8 (August 11, 2021): 240. http://dx.doi.org/10.3390/horticulturae7080240.
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Environmental conditions and nutritional stress may greatly affect crop performance. Abiotic stresses such as temperature (cold, heat), water (drought, flooding), irradiance, salinity, nutrients, and heavy metals can strongly affect plant growth dynamics and the yield and quality of horticultural products. Such effects have become of greater importance during the course of global climate change. Different strategies and techniques can be used to detect, investigate, and mitigate the effects of environmental and nutritional stress. Horticultural crop management is moving towards digitized, precision management through wireless remote-control solutions, but data analysis, although a traditional approach, remains the basis of stress detection and crop management. This Special Issue summarizes the recent progress in agronomic management strategies to detect and reduce environmental and nutritional stress effects on the yield and quality of horticultural crops.
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Han, Xiaoyue, Yue Wang, Yan Huang, Xiaoyan Wang, Jaebum Choo, and Lingxin Chen. "Fluorescent probes for biomolecule detection under environmental stress." Journal of Hazardous Materials 431 (June 2022): 128527. http://dx.doi.org/10.1016/j.jhazmat.2022.128527.
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Kolluru, Vindhya Rani, and Satwik Naidu Yedla. "Posture and stress detection system using open CV and media pipe." i-manager's Journal on Computer Science 12, no. 2 (2024): 8. http://dx.doi.org/10.26634/jcom.12.2.20637.
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Posture and stress are two critical factors affecting a person's physical and mental well-being. Proper posture and stress management can help avoid a range of health issues. Poor posture may result in chronic discomfort, decreased mobility, and an increased risk of musculoskeletal problems. Similarly, stress can negatively impact physical and mental health, contributing to conditions such as depression, anxiety, and cardiovascular disease. Traditional methods for assessing posture and stress, including physical examinations or self-reporting, can be subjective and time-consuming. Recent advances in machine learning and computer vision techniques have enabled the development of models that automatically detect posture and stress levels from video data. In this study, a Django framework was built that incorporates models to assess posture by calculating angles between tracked distance vectors. Stress levels are evaluated based on facial features and expressions. The use of skeleton data for human posture recognition is a key research area in human-computer interaction. By employing the MSR 3D Action Dataset, 33 key skeletal points on the body are detected, aiding in posture determination. Additionally, analyzing facial features and emotions is essential for estimating stress levels. The approach relies on convolutional neural networks.
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Can, Yekta Said, Heather Iles-Smith, Niaz Chalabianloo, Deniz Ekiz, Javier Fernández-Álvarez, Claudia Repetto, Giuseppe Riva, and Cem Ersoy. "How to Relax in Stressful Situations: A Smart Stress Reduction System." Healthcare 8, no. 2 (April 16, 2020): 100. http://dx.doi.org/10.3390/healthcare8020100.
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Stress is an inescapable element of the modern age. Instances of untreated stress may lead to a reduction in the individual’s health, well-being and socio-economic situation. Stress management application development for wearable smart devices is a growing market. The use of wearable smart devices and biofeedback for individualized real-life stress reduction interventions has received less attention. By using our unobtrusive automatic stress detection system for use with consumer-grade smart bands, we first detected stress levels. When a high stress level is detected, our system suggests the most appropriate relaxation method by analyzing the physical activity-based contextual information. In more restricted contexts, physical activity is lower and mobile relaxation methods might be more appropriate, whereas in free contexts traditional methods might be useful. We further compared traditional and mobile relaxation methods by using our stress level detection system during an eight day EU project training event involving 15 early stage researchers (mean age 28; gender 9 Male, 6 Female). Participants’ daily stress levels were monitored and a range of traditional and mobile stress management techniques was applied. On day eight, participants were exposed to a ‘stressful’ event by being required to give an oral presentation. Insights about the success of both traditional and mobile relaxation methods by using the physiological signals and collected self-reports were provided.
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Mariyati, Mariyati, and Priharyanti Wulandari. "Empowerment of nurses in primary health service in early detection of mental health and stress management of pregnant women." Community Empowerment 7, no. 11 (December 6, 2022): 1911–17. http://dx.doi.org/10.31603/ce.7466.
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Pregnant women are a vulnerable group for mental health problems. This community service aims to increase the involvement of nurses in early detection of mental health and stress management for pregnant women. The methods used include outreach, health education, training and demonstrations on early detection of mental health and its treatment. The targets of the community service program are puskesmas nurses and pregnant women who attend antenatal care. The results of the activity show that nurses are able to carry out early detection of mental health with a self-reporting questionnaire. The results of early detection showed that 7 pregnant women who visited the Puskesmas Rowosari for antenatal care had mental health problems characterized by feeling anxious about their first pregnancy, physical changes, delivery process, and condition of the fetus and helpless due to lack of husband's support. After being given stress management in the form of imagery guides, progressive muscle relaxation and yoga, pregnant women said they were calmer, thought positively, and had less anxiety. In addition, knowledge and skills regarding mental health also increased.
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Chen, Jerry, Maysam Abbod, and Jiann-Shing Shieh. "Pain and Stress Detection Using Wearable Sensors and Devices—A Review." Sensors 21, no. 4 (February 3, 2021): 1030. http://dx.doi.org/10.3390/s21041030.
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Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This review presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors. Various physiological signals (i.e., heart activity, brain activity, muscle activity, electrodermal activity, respiratory, blood volume pulse, skin temperature) and behavioral signals are organized for wearables sensors detection. By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection. Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or devices. Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue.
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Feng, Ziheng, Haiyan Zhang, Jianzhao Duan, Li He, Xinru Yuan, Yuezhi Gao, Wandai Liu, Xiao Li, and Wei Feng. "Improved Spectral Detection of Nitrogen Deficiency and Yellow Mosaic Disease Stresses in Wheat Using a Soil Effect Removal Algorithm and Machine Learning." Remote Sensing 15, no. 10 (May 10, 2023): 2513. http://dx.doi.org/10.3390/rs15102513.
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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.
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Yuan, Feiyan, Hang Zhang, and Tonghai Liu. "Stress-Free Detection Technologies for Pig Growth Based on Welfare Farming: A Review." Applied Engineering in Agriculture 36, no. 3 (2020): 357–73. http://dx.doi.org/10.13031/aea.13329.
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Abstract. The detection of pig growth and monitoring of abnormal behaviors are key steps in pig breeding management. Using conventional methods to obtain information on growth and abnormal behavior causes stress to pigs, directly affects the number of live pigs for market, and decreases the quality of the pork. Moreover, this approach requires considerable labor, reduces economic returns, and does not meet the requirements of high-welfare farming. Compared to the conventional methods for obtaining growth parameters and data on abnormal behaviors, modern information technology provides a new method for stress-free growth detection and behavior monitoring in farmed pigs. This article first summarizes the importance of body size, body mass, and abnormal behaviors as well as the correlations among these factors. For the research on growth detection and behavior monitoring based on computer vision, radio frequency identification (RFID) and sensor technology, methods of detecting increases in body size and body mass and methods of monitoring abnormal behaviors are summarized separately. Through computer-computer vision technology, we found that the data sampling for growth and abnormal behaviors of the pigs was achieved without contact monitoring but, rather, occurred at the expense of complex data calculation and a higher illumination requirement during data collection. However, with the development of depth camera technology and improved product performance, technology based on high-precision depth cameras reduces the amount of data processing and complexity, making it possible to obtain real-time data on pig growth and abnormal behaviors. Moreover, with the advantages of no contact and no stress, the method conforms to the requirements of welfare farming. Keywords: Abnormal behaviors, Stress-free detection, Welfare farming.
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KHAN, MA, R. GUL, and E. IRSHAD. "EFFECT OF STRESS MANAGEMENT ON LEVEL OF DEPRESSION, ANXIETY AND STRESS IN CARDIOVASCULAR DISEASE PATIENTS." Biological and Clinical Sciences Research Journal 2024, no. 1 (October 31, 2024): 1252. http://dx.doi.org/10.54112/bcsrj.v2024i1.1252.
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Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality worldwide, making holistic health management essential for patients. Psychological well-being, particularly stress, depression, and anxiety management, is critical for effective CVD rehabilitation. Interventions focused on stress management hold promise for reducing these negative psychological impacts, thereby enhancing patient health and recovery. Objective: This study aimed to evaluate the effect of a structured stress management intervention on reducing stress, depression, and anxiety levels in patients with cardiovascular disease. Methods: A pretest-posttest design with a control group was employed to assess the impact of stress management on mental health outcomes in CVD patients. Sixty patients with CVD were recruited from a private and a government hospital in Rawalpindi, Pakistan, in 2023. Patients were randomly assigned to either a control group (n=30) or an intervention group (n=30). The intervention group participated in eight weekly 90-minute sessions focused on stress, thought-feeling connections, relaxation techniques, cognitive distortion detection, management of anxiety and depression, social relations enhancement, and effective coping strategies. Data were collected at baseline (pretest), 1.5 months, and 3 months post-intervention using validated scales to measure anxiety, stress, and depression levels. The control group received standard care without any psychological intervention. Statistical analysis was performed using SPSS version 25, and repeated measures ANOVA was applied to evaluate changes in stress, depression, and anxiety levels over time. Results: The results revealed a significant reduction in stress, depression, and anxiety levels in the intervention group compared to the control group (p<0.05). Participants who underwent the stress management intervention showed substantial improvement in psychological well-being at both the 1.5-month and 3-month follow-ups. Conclusion: The stress management intervention demonstrated a significant positive impact on reducing anxiety, stress, and depression in CVD patients. These findings highlight the value of incorporating structured psychological support as a part of comprehensive care for cardiovascular patients, providing essential guidance for healthcare practitioners to enhance patient outcomes through holistic care.