Designing Visualizations for Workplace Stress Management: Results of a Pilot Study at a Swiss Municipality

Stepanovic, Stefan; Mozgovoy, Vadym; Mettler, Tobias

doi:10.1007/978-3-030-27325-5_8

Cited by 9 publications

(20 citation statements)

References 25 publications

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“…The approach used in this study is comparable in longitudinal estimation steps to those used by other researchers. The present research reflects Study 3 [4] and Study 4 [5] involving three estimation steps: identification, measurement and visualization. The evaluation framework is similar to that found in Study 5 [3] and Study 6 [6] involving four estimation steps.…”

Section: Measurement and Visualizationmentioning

confidence: 99%

“…However, previous research has not clearly established the accuracy of measurements based on indirect measurements through unsupervised algorithms [3][4][5][6] and has not formalized the process of longitudinal evaluation of stress-related states.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, validation is not always performed based on remote stress identification [4,5]. However, longitudinal evaluation of stress-related states using bio-sensor data requires validated measurements.…”

Section: Introductionmentioning

confidence: 99%

“…The measurement of stress-related signals can be direct [18,19] or indirect [3][4][5][6]. Direct measurement is usually conducted through isolated signals, while the stress level can be determined based on the level of variable(s) associated with stress.…”

Section: Introductionmentioning

confidence: 99%

“…Other researchers used indirect measurement with ML algorithms. Some indirect algorithm-based measures follow a fully unsupervised approach [4,5] that does not include validation. Two recent studies include implied IW validation [3,6].…”

Section: Introductionmentioning

confidence: 99%

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Longitudinal estimation of stress-related states through bio-sensor data

Mozgovoy

2021

ACI

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PurposeThe authors aim to develop a conceptual framework for longitudinal estimation of stress-related states in the wild (IW), based on the machine learning (ML) algorithms that use physiological and non-physiological bio-sensor data.Design/methodology/approachThe authors propose a conceptual framework for longitudinal estimation of stress-related states consisting of four blocks: (1) identification; (2) validation; (3) measurement and (4) visualization. The authors implement each step of the proposed conceptual framework, using the example of Gaussian mixture model (GMM) and K-means algorithm. These ML algorithms are trained on the data of 18 workers from the public administration sector who wore biometric devices for about two months.FindingsThe authors confirm the convergent validity of a proposed conceptual framework IW. Empirical data analysis suggests that two-cluster models achieve five-fold cross-validation accuracy exceeding 70% in identifying stress. Coefficient of accuracy decreases for three-cluster models achieving around 45%. The authors conclude that identification models may serve to derive longitudinal stress-related measures.Research limitations/implicationsProposed conceptual framework may guide researchers in creating validated stress-related indicators. At the same time, physiological sensing of stress through identification models is limited because of subject-specific reactions to stressors.Practical implicationsLongitudinal indicators on stress allow estimation of long-term impact coming from external environment on stress-related states. Such stress-related indicators can become an integral part of mobile/web/computer applications supporting stress management programs.Social implicationsTimely identification of excessive stress may improve individual well-being and prevent development stress-related diseases.Originality/valueThe study develops a novel conceptual framework for longitudinal estimation of stress-related states using physiological and non-physiological bio-sensor data, given that scientific knowledge on validated longitudinal indicators of stress is in emergent state.

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Section: Measurement and Visualizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Longitudinal estimation of stress-related states through bio-sensor data

Mozgovoy

2021

ACI

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Mental Health Monitoring at Work: IoT Solutions and Privacy Concerns

Naous

Mettler

2022

Communications in Computer and Information Science

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The fast-paced business environment and new work arrangements have elevated mental health risks, especially occupational stress and burnouts. Mental health becomes a critical aspect for occupational safety and health. Therefore, employers aim to improve employees' well-being and safety at the workplace through dedicated health initiatives. Internet-of-things (IoT) technology is increasingly being used for such purposes. However, its implementation in the workplace is accompanied by privacy concerns related to sensitive data collection. In this study, we provide a meta-synthesis of existing IoT solutions for supporting employees' mental health in office settings. We classify existing studies into use cases with possible implementation options. We also discuss main challenges emerging from privacy concerns along the IoT data lifecycle. We emphasize the opportunity for the connected workplace to improve occupational health in the future workplace,

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AI Systems for Occupational Safety and Health: From Ethical Concerns to Limited Legal Solutions

Weerts

Naous

Bouchikhi

et al. 2022

Lecture Notes in Computer Science

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Digital technologies in the workplace have undergone a remarkable evolution in recent years. Biosensors and wearables that enable data collection and analysis, through artificial intelligence (AI) systems are becoming widespread in the working environment, whether private or public. These systems face strong critics in the media and academia, emphasizing the algorithmic management trend. However, they can also be deployed for the common good such as occupational safety and health (OSH). In this sense, they can be promoted by public authorities in a public policy perspective of OSH, and they can also be used by public employers as a tool to improve the health of workers. Nevertheless, we argue that AI systems for OSH do not exclude thorny problems, considering the sensitive data collected, potential chilling effects, and employment discrimination. Based on three realistic scenarios, we identify a series of ethical concerns raised by the use of such AI systems and elaborate on the legal responses to these issues based on existing European law. With this analysis, we highlight blind spots, that is, situations in which existing laws do not provide clear or satisfying answers to relevant ethical concerns. We conclude that other avenues should be investigated to help the public sector determine whether it is legally and socially acceptable to deploy AI systems and achieve their public policy.

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Designing Visualizations for Workplace Stress Management: Results of a Pilot Study at a Swiss Municipality

Cited by 9 publications

References 25 publications

Longitudinal estimation of stress-related states through bio-sensor data

Longitudinal estimation of stress-related states through bio-sensor data

Mental Health Monitoring at Work: IoT Solutions and Privacy Concerns

AI Systems for Occupational Safety and Health: From Ethical Concerns to Limited Legal Solutions

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