User Stress Research Articles

Depression and Disclosure Behavior via Social Media using CNN and NLP

Abstract: Nowadays, mental stress has become a danger to human progress. As life moves fast, more and more people feel uncomfortable. It is not always good to understand customer concern early and keep customers safe. Thanks to informal digital communication, people take the time to share some of their experiences and talk to friends through digital systems and media companies, making it possible to have online relationships that involve anxiety identity. In our paper, we found that in web-based omnimedia the user's stress state is related to the stress of his friends, and I use continuous data from appropriate levels to avoid always acknowledging the client's stress and relationship. Discussion In our system, we see that customer's kingdom anxiety is related to their friends' digital kingship anxiety, and we use real advanced levels of important certification information to understand user stress and business relationships. together. As a shift in thinking, I started by unpacking many of the tensions around print, visual, and social, and proposed a format in which we could experiment with Facebook ads using the form of a neural network. We can understand if the customer is upset. After asking whether the patient is afraid or not, the assistant's opinion of the clinic should be checked, and the administrator can send a safe list to the user so that he can strengthen and shine in daily life. -CNN, classification, depression, machine learning;

Hybrid deep learning model for wearable sensor‐based stress recognition for Internet of Medical Things (IoMT) system

SummaryThere are numerous, interrelated, and multi‐dimensional aspects that influence a person's mental health, one of them being stress. Smart wearable technology having physiological and motion sensors has paved the way for real‐time data collection to deliver cutting‐edge information about the stress of individuals. It is now possible to build an Internet of Medical Things (IoMT) system that can recognise the user's stress, revealing the elements that cause stress. However, there are significant gaps in the existing system for stress recognition. To begin with, stress recognition is primarily studied for a specific set of people, such as occupational stress among office working persons or hospital staff during emergency duties, and so forth. Second, most past work on stress recognition has focused on extracting handcrafted traits, which necessitates human interaction and skill. To overcome above mentioned challenges, this work proposes a novel IoMT framework for continuous stress recognition for mental well‐being. This paper presents a hybrid deep learning (DL) approach for automatically retrieving features and classification into various stress states for the IoMT system to address these difficulties. The proposed expanded system gathers data from wearable physiological sensors and feeds it into a convolutional neural network–long short‐term memory (CNN‐LSTM), a hybrid DL classifier. The suggested approach has been tested on the wearable stress and affect detection (WESAD) dataset. It has a reported accuracy of 90.45%, which is higher than previously reported accuracies from existing machine learning (ML) and DL approaches.

Stress Detection using Deep Learning Techniques

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.

Studying the Influence of Toxicity and Emotion Features for Stress Detection on Social Media

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.

Does the user behavior effect the productivity of hammer drilling? – Analysis of the influences of feed and lateral force

The objective of this study was to investigate the relationship between feed and lateral force with productivity in hammer drilling. Necessary user forces and vibration caused by hammer drilling leads to user fatigue and long-term injuries. Through an increase in productivity, the stress duration and thus injuries caused can be reduced. The user, who influences productivity, applies lateral forces in addition to the feed force during hammer drilling. Their influence and interaction with the feed force on productivity has not yet been investigated. In this study, a total of 1152 boreholes were performed on an automated test bench. Along with the feed and lateral forces, the setup, consisting of hammer drill and drill bit model, was varied in order to investigate interaction effects as well as discuss transferability of the findings. The productivity was evaluated by the rate of penetration (ROP). It was observed that the ROP decreased with increasing lateral forces (p < .001, r = 0.315) and increased with increasing feed force. The detailed courses of these relationships were setup-specific. At low feed forces, the feed and lateral force interacted on the ROP. The investigated relationships indicate an efficient operating range depending on the user forces and setup used, which enables a reduction of the user's stress duration. The findings help engineers develop power tools that provide more efficient and hence less fatiguing work, making them more ergonomic for the user.

WiStress

Stress plays a critical role in our lives, impacting our productivity and our long-term physiological and psychological well-being. This has motivated the development of stress monitoring solutions to better understand stress, its impact on productivity and teamwork, and help users adapt their habits toward more sustainable stress levels. However, today's stress monitoring solutions remain obtrusive, requiring active user participation (e.g., self-reporting), interfering with people's daily activities, and often adding more burden to users looking to reduce their stress. In this paper, we introduce WiStress, the first system that can passively monitor a user's stress levels by relying on wireless signals. WiStress does not require users to actively provide input or to wear any devices on their bodies. It operates by transmitting ultra-low-power wireless signals and measuring their reflections off the user's body. WiStress introduces two key innovations. First, it presents the first machine learning network that can accurately and robustly extract heartbeat intervals (IBI's) from wireless reflections without constraints on a user's daily activities. Second, it introduces a stress classification framework that combines the extracted heartbeats with other wirelessly captured stress-related features in order to infer a subject's stress level. We built a prototype of WiStress and tested it on 22 different subjects across different environments in both stress-induced and free-living conditions. Our results demonstrate that WiStress has high accuracy (84%-95%) in inferring a person's stress level in a fully-automated way, paving the way for ubiquitous sensing systems that can monitor stress and provide feedback to improve productivity, health, and well-being.

Gamification in e-learning: The mitigation role in technostress

The digital world demands graduates who are accustomed to deal with technology. Blended learning is one of the strategies by combining online media with face-to-face classes. It cannot be denied that students who interact with technology experience stress and tension. This condition have an impact on the learning process so that a way out is needed to bring it down. Gamification is a gaming technique that is applied to non-game applications to increase pleasure when interacting with these applications. This feature has been implemented in business applications, social media, e-commerce, and e-learning. However, the impact of playfulness in mitigating technostress has not been studied. This research examined the role of feedback mechanism and presentation mechanism in giving pleasure in LMS. Furthermore, this playfulness is expected to reduce the stress experienced by users. The research was conducted using a quasi-experimental method by giving participants time to follow the course with the gamification feature. The results showed that the gamification mechanism is able to provide pleasure which in turn will reduce the user's stress level. Based on the user-perceived of playfulness, gamification can reduce stress levels so it will reduce user resistance and increase the effectiveness of technology implementation.

The Effects of MMORPG User's Stress Coping Strategies on Continued Use Intention Through Self-Control and Behavioral Adaptation

The Effects of MMORPG User's Stress Coping Strategies on Continued Use Intention Through Self-Control and Behavioral Adaptation

Stress Detection Using Physiological Sensors

Physiological signals such as electrodermal activity and heart rate can help computing systems detect a user's stress level. Integrating additional measures not yet exploited by such systems could significantly increase stress-detection accuracy, enabling many new applications.

Automatic stress-relieving music recommendation system based on photoplethysmography-derived heart rate variability analysis.

This paper presents an automatic stress-relieving music recommendation system (ASMRS) for individual music listeners. The ASMRS uses a portable, wireless photoplethysmography module with a finger-type sensor, and a program that translates heartbeat signals from the sensor to the stress index. The sympathovagal balance index (SVI) was calculated from heart rate variability to assess the user's stress levels while listening to music. Twenty-two healthy volunteers participated in the experiment. The results have shown that the participants' SVI values are highly correlated with their prespecified music preferences. The sensitivity and specificity of the favorable music classification also improved as the number of music repetitions increased to 20 times. Based on the SVI values, the system automatically recommends favorable music lists to relieve stress for individuals.

무선 심전도측정을 통한 스트레스 평가에 관한 연구

본 연구에서는 일상생활 속에서의 심전도를 측정하여 HRV(Heart Rate Variability)를 모니터링 하며 스트레스를 추정할 수 있는 시스템을 제안한다. 제안된 무선 생체 측정기 모듈은 전처리필터와 BFP사용으로 잡음은 감소 시키면서 신호의 크기는 증가시키기는 회로를 설계하였고, ECG를 측정하여 R-wave를 추출하고 이를 통한 HRV로 인간의 감성 중 스트레스를 평가하는 알고리즘을 개발하였다. 또한 무선 생체 측정기 시스템은 활동 모니터링을 위해 휴대하기 편한 사각형의 작은 사이즈로 구성되며 측정방법이 간단하여 언제든지 측정이 가능하다. 실험을 통해서 취득한 사용자의 HRV 정보는 스트레스 평가 지수 도출 알고리즘을 통한 스트레스를 추정할 수 있으며 스트레스 부하 프로토콜을 수행 한 전후를 비교 분석하여 많은 파라미터에서 유의미한 차이가 나타났다. 본 연구에서 수행된 실험은 일상생활 중에 심장의 전기 활동을 모니터링 할 수 있는무선 생체측정기를 개발하였으며 이를 이용하여 시간영역 분석 및 주파수 영역 분석을 통하여 스트레스 지수를 평가 할 수 있는 알고리즘 시스템은 건강 모니터링 시스템으로 활용도가 높을 것으로 기대된다. In this paper, daily life stress monitoring system is proposed. The proposed wireless ECG module, reducing the noise and increasing the size of signal, amplification circuit was designed for. Using HRV(Heart Rate Variability), extracted by measuring R-wave, stress diagnostic algorithms to assess the stress of human emotion were developed. For monitoring the activities, the proposed system is consist of small rectangular size for portable and by simple measurement it is possible to measure at any time. Through experiments, the proposed approach to represent user's stress level can be confirmed. Through that, it can see appropriate structure to obtain R-wave for stress assess as well as high resemblance to the clinical electrocardiogram. In this paper, performed experiments was developed nonrestraint measuring and wearable wireless biometric scanner that is able to monitor the heart's electrical activity of everyday life.Using this, the algorithm system, that is able to assess stress index through time-domain and frequency-domain analysis of the front and the rear of performing stress load protocol, was developed,

Auditory Warnings in the Medical Work Domain: An Overview of Critical Issues

This presentation provides an overview of domain specific and more general issues critical to the successful design of auditory warnings for medical devices. Successful design requires a thorough understanding of the unique aspects of the medical environment as well as an appreciation of the varied needs and abilities of the diverse user population (e.g., physicians, nurses, patients, etc.). Auditory warnings in the medical arena should not only assist in event detection but also facilitate diagnosis and treatment. False alarms remain an important concern. Many clinical devices now employ integrated or centralized warning strategies, and standardized auditory tones. With these devices, it is necessary to observe the visual display to mentally discern and prioritize all active warnings. A better approach in some circumstances may be the use of novel information containing warnings (e.g., “earcons” or encoded auditory displays). Additionally, there is a need for more “intelligent” warnings based on patient- and situation-(context) specific information. Adjusting warning thresholds as each patient's condition evolves would decrease false alarms, mitigate noise pollution, and reduce user's stress and workload. Regardless, a user-centered HFE design process should be used whereby all proposed auditory warnings are designed in an iterative fashion with continuous user input and evaluation, including rigorous testing by HFE professionals of end-users in the expected use environment.

Measurement of stress to gauge user satisfaction with features of the computer interface

Abstract Human stress in a computer-related job situation can result from a variety of causes. A comprehensive cause-and-effect model is developed, using extensive confirmation from the literature. Then, the components of user satisfaction are defined and a conceptual measure of user satisfaction is constructed. These definitions use the same cause-and-effect relationships that were identified for user stress. Based on the conjecture that stress, or lack thereof, is an appropriate indicator of the user's level of (dis)satisfaction with a system, a measurement methodology is proposed. It is intended to determine both how satisfied a user is with an interface and what the causes of (dis)satisfaction might be. Techniques for obtaining objective indicators of the user's stress levels are surveyed. A general experimental procedure is outlined and its verification is described. The methodology is considered to be particularly important for application to critical, high-pressure jobs, such as those in air traffic control and in the n,uclear and chemical industries.