Electrodermal Activity based Classification of Induced Stress in a Controlled Setting

S, Amalan; Shyam, A; Anusha, A S; Preejith, S P; Tony, Akl; Joseph, Jayaraj; Mohanasankar, Sivaprakasam

doi:10.1109/memea.2018.8438703

Cited by 17 publications

(12 citation statements)

References 12 publications

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“…6(a) and (b)), while the skin temperature values did not significantly change between the baseline, stress, and recovery periods. These results are consistent with physiological reactivity to the TSST shown in previous literature [51], [110], [111] and indicate an activation of the hypothalamic-pituitary-adrenal axis and the sympathetic branch of the ANS during the stress tasks [112]. Importantly, subjects' perceived anxiety and ''in the moment'' stress levels were consistent with the wearable results (Fig.…”

Section: Discussionsupporting

confidence: 92%

“…The study aimed to investigate the feasibility of using wristworn wearable data, normally available through researchgrade and commercial off-the-shelf devices, together with self-ratings of stress and anxiety to identify acute stress using deep learning methods. Previous studies have demonstrated the potential for wearable data streams of measures such as EDA, HR, and HRV to detect acute stress events using machine learning methods [51], [56], [57], [109]. In this study, we compared the results of a deep learning model using only wearable data and wearable in combination with survey data to detect moments of stress.…”

Section: Discussionmentioning

confidence: 99%

“…Villarejo et al [49], were able to correctly classify a stress state from a non-stress state with a 76% success rate using EDA as the main predictor after inducing stress through different tasks that required mental effort. Similarly, Zangroniz et al [50] and Amalan et al [51], showed that supervised learning models using data from wearable devices collecting solely EDA were able to differentiate between a stressful and calm condition with 89% and 93% accuracy, respectively. Zangroniz et al [50], and Amalan et al [51], induced stress using a database of high arousal photographs and the Trier Social Stress Test (TSST), respectively.…”

Section: Introductionmentioning

confidence: 93%

“…Similarly, Zangroniz et al [50] and Amalan et al [51], showed that supervised learning models using data from wearable devices collecting solely EDA were able to differentiate between a stressful and calm condition with 89% and 93% accuracy, respectively. Zangroniz et al [50], and Amalan et al [51], induced stress using a database of high arousal photographs and the Trier Social Stress Test (TSST), respectively. Setz et al [52] also demonstrated an 82.8% maximum accuracy in discriminating stress from cognitive load using only EDA features, while Kurniawan et al [53], and Anusha et al [54], achieved a 75% precision and 85% accuracy in acute stress classification using only EDA and complex classifiers.…”

Section: Introductionmentioning

confidence: 93%

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The Feasibility of Wearable and Self-Report Stress Detection Measures in a Semi-Controlled Lab Environment

et al. 2021

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 93%

See 2 more Smart Citations

The Feasibility of Wearable and Self-Report Stress Detection Measures in a Semi-Controlled Lab Environment

et al. 2021

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“…It was decided to use different well-known classifiers, which were grouped into trees, ensemble, regression, discriminant, naïve Bayes, k-nearest neighbors (KNN) and support vector machines (SVM). In addition, several standard configurations were chosen [52][53][54][55][56]. More concretely, we used logistic regression and linear discriminant classifier.…”

Section: Comparison Of Arousal Detection and Sam Questionnaire Responsesmentioning

confidence: 99%

Arousal Detection in Elderly People from Electrodermal Activity Using Musical Stimuli

Bartolomé-Tomás

Sánchez-Reolid

Fernández-Sotos³

et al. 2020

Sensors

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The detection of emotions is fundamental in many areas related to health and well-being. This paper presents the identification of the level of arousal in older people by monitoring their electrodermal activity (EDA) through a commercial device. The objective was to recognize arousal changes to create future therapies that help them to improve their mood, contributing to reduce possible situations of depression and anxiety. To this end, some elderly people in the region of Murcia were exposed to listening to various musical genres (flamenco, Spanish folklore, Cuban genre and rock/jazz) that they heard in their youth. Using methods based on the process of deconvolution of the EDA signal, two different studies were carried out. The first, of a purely statistical nature, was based on the search for statistically significant differences for a series of temporal, morphological, statistical and frequency features of the processed signals. It was found that Flamenco and Spanish Folklore presented the highest number of statistically significant parameters. In the second study, a wide range of classifiers was used to analyze the possible correlations between the detection of the EDA-based arousal level compared to the participants’ responses to the level of arousal subjectively felt. In this case, it was obtained that the best classifiers are support vector machines, with 87% accuracy for flamenco and 83.1% for Spanish Folklore, followed by K-nearest neighbors with 81.4% and 81.5% for Flamenco and Spanish Folklore again. These results reinforce the notion of familiarity with a musical genre on emotional induction.

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Current trends and opportunities in the methodology of electrodermal activity measurement

Tronstad¹,

Amini²,

Bach³

et al. 2022

Physiol. Meas.

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Electrodermal activity (EDA) has been measured in the laboratory since the late 1800s. Although the influence of sudomotor nerve activity and the sympathetic nervous system on EDA is well established, the mechanisms underlying EDA signal generation are not completely understood. Owing to simplicity of instrumentation and modern electronics, these measurements have recently seen a transfer from the laboratory to wearable devices, sparking numerous novel applications while bringing along both challenges and new opportunities. In addition to developments in electronics and miniaturization, current trends in material technology and manufacturing have sparked innovations in electrode technologies, and trends in data science such as machine learning and sensor fusion are expanding the ways that measurement data can be processed and utilized. Although challenges remain for the quality of wearable EDA measurement, ongoing research and developments may shorten the quality gap between wearable EDA and standardized recordings in the laboratory. In this topical review, we provide an overview of the basics of EDA measurement, discuss the challenges and opportunities of wearable EDA, and review recent developments in instrumentation, material technology, signal processing, modeling and data science tools that may advance the field of EDA research and applications over the coming years.

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Electrodermal Activity based Classification of Induced Stress in a Controlled Setting

Cited by 17 publications

References 12 publications

The Feasibility of Wearable and Self-Report Stress Detection Measures in a Semi-Controlled Lab Environment

The Feasibility of Wearable and Self-Report Stress Detection Measures in a Semi-Controlled Lab Environment

Arousal Detection in Elderly People from Electrodermal Activity Using Musical Stimuli

Current trends and opportunities in the methodology of electrodermal activity measurement

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