New methods for stress assessment and monitoring at the workplace

The topic of stress is nowadays a very important one, not only in research but on social life in general. People are increasingly aware of this problem and its consequences at several levels: health, social life, work, quality of life, etc. This resulted in a significant increase in the search for devices and applications to measure and manage stress in real-time. Recent technological and scientific evolution fosters this interest with the development of new methods and approaches. In this paper we survey these new methods for stress assessment, focusing especially on those that are suited for the workplace: one of today’s major sources of stress. We contrast them with more traditional methods and compare them between themselves, evaluating nine characteristics. Given the diversity of methods that exist nowadays, this work facilitates the stakeholders’ decision towards which one to use, based on how much their organization values aspects such as privacy, accuracy, cost-effectiveness or intrusivenes

New methods for stress assessment and monitoring at the workplace

The topic of stress is nowadays a very important one, not only in research but on social life in general. People are increasingly aware of this problem and its consequences at several levels: health, social life, work, quality of life, etc. This resulted in a significant increase in the search for devices and applications to measure and manage stress in real-time. Recent technological and scientific evolution fosters this interest with the development of new methods and approaches. In this paper we survey these new methods for stress assessment, focusing especially on those that are suited for the workplace: one of today’s major sources of stress. We contrast them with more traditional methods and compare them between themselves, evaluating nine characteristics. Given the diversity of methods that exist nowadays, this work facilitates the stakeholders’ decision towards which one to use, based on how much their organization values aspects such as privacy, accuracy, cost-effectiveness or intrusivenes

Machine Learning based Stress Detection using Keyboard Typing Behavior

Emotion detection is one of those areas where technological advances have brought about significant changesin the human lifestyle. During COVID-19 pandemic, due to the work from home culture, use of computers and laptop was suddenly increased. Introduction of digital environments gave it a whole new dimension. Emotion detection is a virtual or computerized way to detect stress. People suffer from various kinds of stress in day to day activities and it is directly connected to their performance. The stress factor can be expressed through a number of ways and human behavior.  The way in which humans interact with the computer can reveal the emotional state of the user, mainly the stress. Keyboard typing behavior or characteristics can be used for stress detection. This paper focuses on understanding typing behaviour of human and indicate their stress level. Relevant features are extracted from typing behavior of a user and used for training machine learning models for detection of stress. K-Nearest Neighbor algorithm gave highest accuracy of 84.21% with dimensionality reduction approach

Keeping Calm in the Digital Age: Theorizing on a Self-Monitoring System of Technostress

People spend increasing time interacting with information technologies (IT) due to teleworking, which has become an important cause of psychological stress. Meanwhile, technological advances enable the monitoring of stress via methods that capture individuals’ physiological states like automatic facial expression analysis (AFEA). This research-in-progress article proposes a novel theory that aims at explaining and predicting the impact of AFEA of stress self-monitoring systems on users’ psychological stress. The theory proposes that AFEA of stress self-monitoring systems can increase facial expression self-awareness, and consequently inhibit users’ facial expressions of stress, which can in turn decrease users’ psychological stress. The theory has implications for the design science, affective computing, and technostress domains. It is hoped that the theory will generate discussions on the potential of stress self-monitoring systems in the workplace, education, and society

Understanding collaboration in Global Software Engineering (GSE) teams with the use of sensors: introducing a multi-sensor setting for observing social and human aspects in project management

This paper discusses on-going research in the ways Global Software Engineering (GSE) teams collaborate for a range of software development tasks. The paper focuses on providing the means for observing and understanding GSE team member collaboration including team coordination and member communication. Initially the paper provides the background on social and human issues relating to GSE collaboration. Next the paper describes a pilot study involving a simulation of virtual GSE teams working together with the use of asynchronous and synchronous communication over a virtual learning environment. The study considered the use of multiple data collection techniques recordings of SCRUM meetings, design and implementation tasks. Next, the paper discusses the use of a multi-sensor for observing human and social aspects of project management in GSE teams. The scope of the study is to provide the means for gathering data regarding GSE team coordination for project managers including member emotions, participation pattern in team discussions and potentially stress levels

Understanding collaboration in Global Software Engineering (GSE) teams with the use of sensors: introducing a multi-sensor setting for observing social and human aspects in project management

This paper discusses on-going research in the ways Global Software Engineering (GSE) teams collaborate for a range of software development tasks. The paper focuses on providing the means for observing and understanding GSE team member collaboration including team coordination and member communication. Initially the paper provides the background on social and human issues relating to GSE collaboration. Next the paper describes a pilot study involving a simulation of virtual GSE teams working together with the use of asynchronous and synchronous communication over a virtual learning environment. The study considered the use of multiple data collection techniques recordings of SCRUM meetings, design and implementation tasks. Next, the paper discusses the use of a multi-sensor for observing human and social aspects of project management in GSE teams. The scope of the study is to provide the means for gathering data regarding GSE team coordination for project managers including member emotions, participation pattern in team discussions and potentially stress levels

Time Segment Analysis of Heart Rate Variability to Evaluate Daily Stress using Wearable Device Technology

Present studies have successfully evaluated psychological properties such as mental health and stress by using physiological data from the cardiovascular system. Most studies established specific interventions and ambiguous heart rate properties according to homeostatic conditions. We proposed a study evaluating mental stress based on daily activities dataset. Twenty-two healthy men were observed in this study. We employed two approaches based on the time segments, while extracting the HRV parameters. We discovered that there was no significant difference between the parameters corresponding to the daily stress score groups (low- and high-stress) when we used whole-day recording in one segment HRV parameter measurement (p > 0.05). However, by extracting the HRV parameters based on multi time segments (phases 1, 2, and 3), we found parameters that were able to properly distinguish the two groups (low- and high-stress). The frequency domain parameters are the most sensitive features, especially the LF and HF (p < 0.01), followed by the total power (p < 0.05). In the time domain measurement, the RMSSD, StdHR, SD1, and SD2 are able to differentiate the participants based on the daily stress scores (p < 0.05). As a result, this study proposed that by continually monitoring biological signals based on time segment and employing the given parameters, it is possible to appropriately and meaningfully measure the daily stress condition for future classification studies

Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This research aims to identify a feasible model to predict a learner’s stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner’s emotions. The few signals produced by mouse and keyboard could enable such solution to measure real world individual’s affective states. It is also important to ensure that the measurement can be applied regardless the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes of duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification

Investigating Employees’ Concerns and Wishes Regarding Digital Stress Management Interventions With Value Sensitive Design: Mixed Methods Study

Background: Work stress places a heavy economic and disease burden on society. Recent technological advances include digital health interventions for helping employees prevent and manage their stress at work effectively. Although such digital solutions come with an array of ethical risks, especially if they involve biomedical big data, the incorporation of employees' values in their design and deployment has been widely overlooked. Objective: To bridge this gap, we used the value sensitive design (VSD) framework to identify relevant values concerning a digital stress management intervention (dSMI) at the workplace, assess how users comprehend these values, and derive specific requirements for an ethics-informed design of dSMIs. VSD is a theoretically grounded framework that front-loads ethics by accounting for values throughout the design process of a technology. Methods: We conducted a literature search to identify relevant values of dSMIs at the workplace. To understand how potential users comprehend these values and derive design requirements, we conducted a web-based study that contained closed and open questions with employees of a Swiss company, allowing both quantitative and qualitative analyses. Results: The values health and well-being, privacy, autonomy, accountability, and identity were identified through our literature search. Statistical analysis of 170 responses from the web-based study revealed that the intention to use and perceived usefulness of a dSMI were moderate to high. Employees' moderate to high health and well-being concerns included worries that a dSMI would not be effective or would even amplify their stress levels. Privacy concerns were also rated on the higher end of the score range, whereas concerns regarding autonomy, accountability, and identity were rated lower. Moreover, a personalized dSMI with a monitoring system involving a machine learning-based analysis of data led to significantly higher privacy (P=.009) and accountability concerns (P=.04) than a dSMI without a monitoring system. In addition, integrability, user-friendliness, and digital independence emerged as novel values from the qualitative analysis of 85 text responses. Conclusions: Although most surveyed employees were willing to use a dSMI at the workplace, there were considerable health and well-being concerns with regard to effectiveness and problem perpetuation. For a minority of employees who value digital independence, a nondigital offer might be more suitable. In terms of the type of dSMI, privacy and accountability concerns must be particularly well addressed if a machine learning-based monitoring component is included. To help mitigate these concerns, we propose specific requirements to support the VSD of a dSMI at the workplace. The results of this work and our research protocol will inform future research on VSD-based interventions and further advance the integration of ethics in digital health

MensSana: Design of a mental well-being self-report interface for shop floor workers

A ascensão da Indústria 4.0 trouxe consigo novas tecnologias e oportunidades que estão a mudar a natureza do trabalho, especialmente em ambientes de chão de fábrica. No entanto, essas mudanças também trouxeram novos desafios para os trabalhadores, incluindo desafios na sua saúde mental. Estes trabalhadores, em particular, enfrentam no seu trabalho estressores físicos e mentais que podem afetar seu bem-estar geral, apesar dos esforços da Indústria 4.0. O conceito de Operador 4.0 na Indústria 4.0 introduz muitos operadores, como o Operador Saudável, que enfatiza a centralidade no ser humano e visa melhorar a eficiência e o bem-estar do trabalhador por meio de tecnologias avançadas e análise de dados. Esta tese propõe o desenvolvimento de uma ferramenta protótipo, co-criada e validada no contexto da Indústria 4.0 para medir métricas do trabalhador e do local de trabalho, criando uma imagem holística do trabalhador, sua competência e bem-estar, alinhado ao conceito de um trabalhador "mais saudável" de Romero et al. Essas informações são devolvidas ao trabalhador e apresentadas de maneira legível e compreensível para identificar tendências e informar decisões futuras relacionadas ao trabalho e bem-estar.The rise of Industry 4.0 has brought about new technologies and opportunities that are changing the nature of work, particularly in factory floor settings. However, these changes have also brought about new challenges for workers, including mental health issues. Shop floor workers, in particular, face physical and mental stressors in their work that can impact their overall well-being, despite Industry 4.0 efforts. The Operator 4.0 concept in Industry 4.0 introduces a lot of operators like the Healthy Operator that emphasises human-centricity and aims to improve worker efficiency and well-being through advanced technologies and data analytics. This thesis proposes the development of a prototype tool co-created and validated in the context of Industry 4.0 to measure metrics from the worker and the workplace, creating a holistic picture of the worker, their competence and well-being in line with Romero's et al. concept of a "healthier" worker. This information is returned to the worker and presented in a readable and understandable manner to identify trends and inform future decisions concerning their work and well-being