Psychological stress level detection based on electrodermal activity

Liu, Yun; Du, Shunda

doi:10.1016/j.bbr.2017.12.021

Cited by 110 publications

(59 citation statements)

References 8 publications

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“…For rock/jazz the one that works better is the RUS boosted with 68.6% accuracy. The results are similar to those found in recent previous studies with EDA [ 60 , 61 ].…”

Section: Resultssupporting

confidence: 93%

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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“…For rock/jazz the one that works better is the RUS boosted with 68.6% accuracy. The results are similar to those found in recent previous studies with EDA [ 60 , 61 ].…”

Section: Resultssupporting

confidence: 93%

Arousal Detection in Elderly People from Electrodermal Activity Using Musical Stimuli

Bartolomé-Tomás

Sánchez-Reolid

Fernández-Sotos³

et al. 2020

Sensors

View full text Add to dashboard Cite

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“…However, the results confirmed the indication that the number of SCR peaks was the only significant measure in the feasibility study in order to measure process model complexity. In general, our results in the context of process model comprehension were in line with related EDA results obtained in other studies with different emphases [47,53,[73][74][75]. Moreover, similar to the correlation between the EDA and the difficulty in tasks shown in the work of [69], there might be a correlation between EDA and process model comprehension resulting in a correlated elevation of the EDA with increasingly complex process models (see Section 4).…”

Section: Discussionsupporting

confidence: 91%

Towards the Applicability of Measuring the Electrodermal Activity in the Context of Process Model Comprehension: Feasibility Study

Winter

Pryss

Probst

et al. 2020

Sensors

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Process model comprehension is essential in order to understand the five Ws (i.e., who, what, where, when, and why) pertaining to the processes of organizations. However, research in this context showed that a proper comprehension of process models often poses a challenge in practice. For this reason, a vast body of research exists studying the factors having an influence on process model comprehension. In order to point research towards a neuro-centric perspective in this context, the paper at hand evaluates the appropriateness of measuring the electrodermal activity (EDA) during the comprehension of process models. Therefore, a preliminary test run and a feasibility study were conducted relying on an EDA and physical activity sensor to record the EDA during process model comprehension. The insights obtained from the feasibility study demonstrated that process model comprehension leads to an increased activity in the EDA. Furthermore, EDA-related results indicated significantly that participants were confronted with a higher cognitive load during the comprehension of complex process models. In addition, the experiences and limitations we learned in measuring the EDA during the comprehension of process models are discussed in this paper. In conclusion, the feasibility study demonstrated that the measurement of the EDA could be an appropriate method to obtain new insights into process model comprehension.

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“…EDA based indices can be used to detect stressful events during driving (Affanni et al, 2018). A recent study utilized feature extraction and discrimination processing techniques to classify EDA data into low, medium, vs. high stress levels with about 82% recognition rate (Liu and Du, 2018). Another recent study found higher SCLs when participants drove a simulated vehicle in autonomous mode compared to manual mode (Morris et al, 2017).…”

Section: Psychophysiological Measures To Assess Cognitive Statesmentioning

confidence: 99%

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

Lohani

Payne

Strayer

2019

Front. Hum. Neurosci.

242

181

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As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems.

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Psychological stress level detection based on electrodermal activity

Cited by 110 publications

References 8 publications

Arousal Detection in Elderly People from Electrodermal Activity Using Musical Stimuli

Arousal Detection in Elderly People from Electrodermal Activity Using Musical Stimuli

Towards the Applicability of Measuring the Electrodermal Activity in the Context of Process Model Comprehension: Feasibility Study

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

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