Requirements for a Reference Dataset for Multimodal Human Stress Detection

Mahesh, Bhargavi; Prassler, Erwin; Hassan, Teena; Garbas, Jens-Uwe

doi:10.1109/percomw.2019.8730884

Cited by 18 publications

(11 citation statements)

References 31 publications

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“…On the other hand, we identified that the generated datasets are not available for later tests or validations; in this sense, the requirements presented by Mahesh et al [ 77 ] can be generalized to build reference datasets.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review for Cognitive State-Based QoE/UX Evaluation

Bañuelos-Lozoya

Serna

González-Franco

et al. 2021

Sensors

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Traditional evaluation of user experience is subjective by nature, for what is sought is to use data from physiological and behavioral sensors to interpret the relationship that the user’s cognitive states have with the elements of a graphical interface and interaction mechanisms. This study presents the systematic review that was developed to determine the cognitive states that are being investigated in the context of Quality of Experience (QoE)/User Experience (UX) evaluation, as well as the signals and characteristics obtained, machine learning models used, evaluation architectures proposed, and the results achieved. Twenty-nine papers published in 2014–2019 were selected from eight online sources of information, of which 24% were related to the classification of cognitive states, 17% described evaluation architectures, and 41% presented correlations between different signals, cognitive states, and QoE/UX metrics, among others. The amount of identified studies was low in comparison with cognitive state research in other contexts, such as driving or other critical activities; however, this provides a starting point to analyze and interpret states such as mental workload, confusion, and mental stress from various human signals and propose more robust QoE/UX evaluation architectures.

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Section: Discussionmentioning

confidence: 99%

A Systematic Review for Cognitive State-Based QoE/UX Evaluation

Bañuelos-Lozoya

Serna

González-Franco

et al. 2021

Sensors

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“…For example, in a recent study, Het et al [ 6 ] used a classic stress paradigm to study the effects of an inpatient treatment on acute stress reactivity in women with eating disorders. In addition, controlled stress induction procedures play an important role in the development of objective stress detection methods [ 7 , 8 ] as they strongly rely on highly qualitative and representative data sets obtained through stress induction experiments [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Inducing and Recording Acute Stress Responses on a Large Scale With the Digital Stress Test (DST): Development and Evaluation Study

et al. 2022

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Background Valuable insights into the pathophysiology and consequences of acute psychosocial stress have been gained using standardized stress induction experiments. However, most protocols are limited to laboratory settings, are labor-intensive, and cannot be scaled to larger cohorts or transferred to daily life scenarios. Objective We aimed to provide a scalable digital tool that enables the standardized induction and recording of acute stress responses in outside-the-laboratory settings without any experimenter contact. Methods On the basis of well-described stress protocols, we developed the Digital Stress Test (DST) and evaluated its feasibility and stress induction potential in a large web-based study. A total of 284 participants completed either the DST (n=103; 52/103, 50.5% women; mean age 31.34, SD 9.48 years) or an adapted control version (n=181; 96/181, 53% women; mean age 31.51, SD 11.18 years) with their smartphones via a web application. We compared their affective responses using the international Positive and Negative Affect Schedule Short Form before and after stress induction. In addition, we assessed the participants’ stress-related feelings indicated in visual analogue scales before, during, and after the procedure, and further analyzed the implemented stress-inducing elements. Finally, we compared the DST participants’ stress reactivity with the results obtained in a classic stress test paradigm using data previously collected in 4 independent Trier Social Stress Test studies including 122 participants overall. Results Participants in the DST manifested significantly higher perceived stress indexes than the Control-DST participants at all measurements after the baseline (P<.001). Furthermore, the effect size of the increase in DST participants’ negative affect (d=0.427) lay within the range of effect sizes for the increase in negative affect in the previously conducted Trier Social Stress Test experiments (0.281-1.015). Conclusions We present evidence that a digital stress paradigm administered by smartphone can be used for standardized stress induction and multimodal data collection on a large scale. Further development of the DST prototype and a subsequent validation study including physiological markers are outlined.

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“…Toward improving the performance of emotion recognition systems, in this work, we aim to answer the following research question: In which extent, public datasets created for emotion recognition systems meet reference requirements? To answer it, we have used -previous justification-the reference dataset requirements for stress detection proposed by Mahesh et al [16]. Then, we evaluated the nine emotions datasets identified by Bota et al [1] against evaluation criteria derived from the requirements.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Physiological Public Datasets for Emotion Recognition Systems

Mendoza

Cuno

Condori-Fernández

et al. 2021

Information Management and Big Data

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Requirements for a Reference Dataset for Multimodal Human Stress Detection

Cited by 18 publications

References 31 publications

A Systematic Review for Cognitive State-Based QoE/UX Evaluation

A Systematic Review for Cognitive State-Based QoE/UX Evaluation

Inducing and Recording Acute Stress Responses on a Large Scale With the Digital Stress Test (DST): Development and Evaluation Study

An Evaluation of Physiological Public Datasets for Emotion Recognition Systems

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