New Methods for Stress Assessment and Monitoring at the Workplace

Carneiro, Davide; Nováis, Paulo; Augusto, Juan Carlos; Payne, Nicola

doi:10.1109/taffc.2017.2699633

Cited by 40 publications

(28 citation statements)

References 79 publications

(77 reference statements)

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“…Another challenge is how to deploy the stress prediction models. The recent reviews on stress recognition [1] 3 https://www.empatica.com/research/e4/ [3] unanimously concluded that due to the physiological difference in how people react to stress, a stress monitoring system should adapt to every individual's physiological needs. The simple, and likely, the most accurate approach is to deploy each person's stress prediction model as a web service (e.g., Representational State Transfer (REST) web service) that can be consumed to predict the person's stress.…”

Section: S T R E S S M O N I T O R I N G I N O F F I C E Smentioning

confidence: 99%

“…Moreover, in the case of physiological evaluations, people are reluctant to reveal their work stress honestly [15]. Luckily, stress spawns detectable physiological, psychological, and behavioral changes that can be used for automatic stress recognition [1] [5]. For example, acute stress decreases a person's Heart Rate Variability (HRV) and his parasympathetic activation [16].…”

Section: Introduction O C U Pa T I O N a L Stress Is Well-mentioning

confidence: 99%

“…Although there is a surfeit of publications [1] [3] [22] [5] on automatic stress prediction, at the moment, aside from a few niche and non-scientifically proven consumer products, there exist no effective system that automatically and unobtrusively monitor people's stress in real-world environments [12]. On the one hand, some of the proposed approaches (e.g., EEG based stress monitoring) are outright impractical because they are too obtrusive.…”

Section: Introduction O C U Pa T I O N a L Stress Is Well-mentioning

confidence: 99%

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Effect of Person-specific Biometrics in Improving Generic Stress Predictive Models

Nkurikiyeyezu¹,

Yokokubo²,

Lopez³

2020

Sensors and Materials

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Because stress is subjective and is expressed differently from one person to another, generic stress prediction models (i.e., models that predict the stress of any person) perform crudely. Only person-specific ones (i.e., models that predict the stress of a preordained person) yield reliable predictions, but they are not adaptable and costly to deploy in real-world environments. For illustration, in an office environment, a stress monitoring system that uses person-specific models would require collecting new data and training a new model for every employee. Moreover, once deployed, the models would deteriorate and need expensive periodic upgrades because stress is dynamic and depends on unforeseeable factors. We propose a simple, yet practical and costeffective calibration technique that derives an accurate and personalized stress prediction model from physiological samples collected from a large population. We validate our approach on two stress datasets. The results show that our technique performs much better than a generic model. For instance, a generic model achieved only a 42.5% ± 19.9% accuracy. However, with only 100 calibration samples, we raised its accuracy to 95.2%±0.5%. We also propose a blueprint for a stress monitoring system based on our strategy, and we debate its merits and limitation. Finally, we made public our source code and the relevant datasets to allow other researchers to replicate our findings.

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Section: S T R E S S M O N I T O R I N G I N O F F I C E Smentioning

confidence: 99%

Section: Introduction O C U Pa T I O N a L Stress Is Well-mentioning

confidence: 99%

Section: Introduction O C U Pa T I O N a L Stress Is Well-mentioning

confidence: 99%

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Effect of Person-specific Biometrics in Improving Generic Stress Predictive Models

Nkurikiyeyezu¹,

Yokokubo²,

Lopez³

2020

Sensors and Materials

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“…The following two sections will briefly present two pilot studies based on the creation of such scenarios. Our work was also driven towards considering stress assessment at the workplace [18].…”

Section: Background Work In Gsdmentioning

confidence: 99%

“…Next, participants were required to select from a list of eighteen (18) topics which ones should be presented. Finally, participants should identify the team member who should present each of the selected topics.…”

Section: Investigating Gse Project Managementmentioning

confidence: 99%

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

Dafoulas

Maia

Ali

et al. 2017

2017 International Conference on Intelligent Environments (IE)

Self Cite

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Abstract-This paper discusses on-going research in the waysGlobal 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.

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Brain and Behavior: Blending of Human and Artificial Minds Toward Stress Recognition and Intervention in Organizational Well‐Being

Solanky¹,

Gupta²

2022

Impact of Artificial Intelligence on Organizational Transformation

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New Methods for Stress Assessment and Monitoring at the Workplace

Cited by 40 publications

References 79 publications

Effect of Person-specific Biometrics in Improving Generic Stress Predictive Models

Effect of Person-specific Biometrics in Improving Generic Stress Predictive Models

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

Brain and Behavior: Blending of Human and Artificial Minds Toward Stress Recognition and Intervention in Organizational Well‐Being

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