Differential effects of physical and psychological stressors on electrodermal activity

Anusha, A S; Joy, Jose; Preejith, S P; Joseph, Jayaraj; Sivaprakasam, Mohanasankar

doi:10.1109/embc.2017.8037868

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…The best feature combination included varEDA, cvEDA, quant_90 (filtered_EDA); quant_75 (detrended_EDA) and cvEDA (diff_EDA). The results matched with that of a similar study, performed on slightly different datasets, reported in a previous publication from the authors [44]. However, a multimodal scheme employing EDA and ST could classify these stressors with a slightly higher accuracy of 96.53%.…”

Section: Effect Of Window Size and Overlap On Classification Accuracysupporting

confidence: 90%

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Physiological signal based work stress detection using unobtrusive sensors

Anusha

Joy

Preejith

et al. 2018

Biomed. Phys. Eng. Express

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Objective: Work stress is identified as the 'health epidemic of 21st century' by WHO because, when left unchecked, it wreaks havoc on human mind and body by accelerating the onset and progression of several health disorders. Hence, the evolution of strategies for early detection of mental stress is pivotal. The study presented here is one step towards the goal of developing a physiological parameter based psychological stress detection scheme which can further be incorporated into a wearable vital signs monitor. Approach: A group of 34 subjects (14 females and 20 males, age: 21.4 ± 1.7 years; mean ± SD) volunteered to participate in a pilot laboratory intervention that emulated real-life job stress scenarios by incorporating stress factors like mental workload, time pressure, performance pressure and social evaluative threat. Electrodermal Activity (EDA), Electrocardiogram (ECG), and Skin Temperature (ST) were monitored throughout the experiment to capture sympathetic activation during stress. Stress response elicitation was validated using salivary cortisol levels. A total of 61 features were extracted from these signals and four classifiers were investigated regarding their ability to detect 'stress' using single and multimodal schemes. A fusion framework that combined the benefits of feature fusion and decision fusion was employed to generate classifier ensembles for multimodal stress detection schemes. As the generated datasets exhibited a class imbalance issue, three separate schemes for class imbalance rectification viz., undersampling, oversampling and SMOTE were investigated concerning their ability to yield the best classification performance. While ECG based performance analysis was restricted to data segments of 300 s duration to conform to international guidelines for short-term HRV analysis, non-overlapping EDA and ST data segments of durations 300 s, 180 s, 60 s, and 30 s were examined to determine the optimum data length that can generate best results. Main Results: EDA gave a superior performance for 60 s windows while ST performed best with data segments of duration 30 s. A comparative study was performed with 25%, 50%, 75% and 90% overlapping data segments as well. However, overlapping did not enhance the performance of the classifiers significantly.While EDA emerged as the best single modality, the highest stress recognition accuracy of 97.13% was yielded by a combination of EDA and ST with data segments of 60 s duration. Furthermore, the differential effect of 'physical' and 'psychological' stressors on EDA and ST was analyzed. Significance: The results clearly suggest that these physiological parameters can not only reliably detect psychological stress but can also discriminate it from physical stress.

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Section: Effect Of Window Size and Overlap On Classification Accuracysupporting

confidence: 90%

“…These 24 datasets can be utilized to study the differential effects of these stressors on recorded physiological signals. The differential effects of these stressors on EDA was analysed by our group and the details can be found in [44].…”

Section: Dataset Summarymentioning

confidence: 99%

Physiological signal based work stress detection using unobtrusive sensors

Anusha

Joy

Preejith

et al. 2018

Biomed. Phys. Eng. Express

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“…For example, if one looks at the VR adaptations of the Trier Social Stress Test mentioned in Section 4.1.4, one finds that in all the listed examples the heart activity is measured. Two other signals frequently used in research to indicate changes in stress level is EDA (Kurniawan et al, 2013;Anusha et al, 2017;Bhoja et al, 2020) and skin temperature (Vinkers et al, 2013;Herborn et al, 2015). Both signals and heart rate variability were compared by Cho et al (2017) in VR.…”

Section: Stressmentioning

confidence: 99%

A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality

Halbig

Latoschik

2021

Front. Virtual Real.

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Measurements of physiological parameters provide an objective, often non-intrusive, and (at least semi-)automatic evaluation and utilization of user behavior. In addition, specific hardware devices of Virtual Reality (VR) often ship with built-in sensors, i.e. eye-tracking and movements sensors. Hence, the combination of physiological measurements and VR applications seems promising. Several approaches have investigated the applicability and benefits of this combination for various fields of applications. However, the range of possible application fields, coupled with potentially useful and beneficial physiological parameters, types of sensor, target variables and factors, and analysis approaches and techniques is manifold. This article provides a systematic overview and an extensive state-of-the-art review of the usage of physiological measurements in VR. We identified 1,119 works that make use of physiological measurements in VR. Within these, we identified 32 approaches that focus on the classification of characteristics of experience, common in VR applications. The first part of this review categorizes the 1,119 works by field of application, i.e. therapy, training, entertainment, and communication and interaction, as well as by the specific target factors and variables measured by the physiological parameters. An additional category summarizes general VR approaches applicable to all specific fields of application since they target typical VR qualities. In the second part of this review, we analyze the target factors and variables regarding the respective methods used for an automatic analysis and, potentially, classification. For example, we highlight which measurement setups have been proven to be sensitive enough to distinguish different levels of arousal, valence, anxiety, stress, or cognitive workload in the virtual realm. This work may prove useful for all researchers wanting to use physiological data in VR and who want to have a good overview of prior approaches taken, their benefits and potential drawbacks.

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“…Dysregulation at the level of CFNAs acts as a promising diagnostic biomarker panel for measuring imbalanced stress and for predicting stress-associated pathologies. According to the World Health Organisation (WHO), stress presents the epidemic of the third millennium [ 16 ]. Accumulated evidence suggests a tight association between chronic stress and psychiatric disorders [ 17 – 21 ].…”

Section: Diagnostic and Prognostic Potential Of Cell-free Nucleic Acimentioning

confidence: 99%

Cell-free nucleic acid patterns in disease prediction and monitoring—hype or hope?

et al. 2020

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Interest in the use of cell-free nucleic acids (CFNAs) as clinical non-invasive biomarker panels for prediction and prevention of multiple diseases has greatly increased over the last decade. Indeed, circulating CFNAs are attributable to many physiological and pathological processes such as imbalanced stress conditions, physical activities, extensive apoptosis of different origin, systemic hypoxic-ischemic events and tumour progression, amongst others. This article highlights the involvement of circulating CFNAs in local and systemic processes dealing with the question, whether specific patterns of CFNAs in blood, their detection, quantity and quality (such as their methylation status) might be instrumental to predict a disease development/progression and could be further utilised for accompanying diagnostics, targeted prevention, creation of individualised therapy algorithms, therapy monitoring and prognosis. Presented considerations conform with principles of 3P medicine and serve for improving individual outcomes and cost efficacy of medical services provided to the population.

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Differential effects of physical and psychological stressors on electrodermal activity

Cited by 8 publications

References 8 publications

Physiological signal based work stress detection using unobtrusive sensors

Physiological signal based work stress detection using unobtrusive sensors

A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality

Cell-free nucleic acid patterns in disease prediction and monitoring—hype or hope?

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