Subject-dependent biosignal features for increased accuracy in psychological stress detection

Giakoumis, Dimitrios; Tzovaras, Dimitrios; Hassapis, George

doi:10.1016/j.ijhcs.2012.10.016

Cited by 40 publications

(24 citation statements)

References 34 publications

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“…However, by combining the response features of HR, GSR, and ST, the performance can be increased, and insights from all physiological signals can be obtained. Furthermore, previous research has suggested that the physiological stress response is person‐dependent, and different participants can show different response levels per physiological signal . For these reasons, and since HR, GSR, and ST are standard measurements readily available in many state‐of‐the art sensors, eg, NeXus 10 MK II, and in multiple wearables such as Empatica E4 (Empatica, Milan, Italy), it is advised to focus further research on the combination of these signals rather than investigate them separately.…”

Section: Discussionmentioning

confidence: 99%

Comparing task‐induced psychophysiological responses between persons with stress‐related complaints and healthy controls: A methodological pilot study

Smets

Schiavone

Velazquez

et al. 2018

Health Science Reports

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AimsChronic stress is an important factor for a variety of health problems, highlighting the importance of early detection of stress‐related problems. This methodological pilot study investigated whether the physiological response to and recovery from a stress task can differentiate healthy participants and persons with stress‐related complaints.Methods and ResultsHealthy participants (n = 20) and participants with stress‐related complaints (n = 12) participated in a laboratory stress test, which included 3 stress tasks. Three physiological signals were recorded: galvanic skin response (GSR), heart rate (HR), and skin temperature (ST). From these signals, 126 features were extracted, including static (eg, mean) and dynamic (eg, recovery time) features. Unsupervised feature selection reduced the set to 26 features. A logistic regression model was developed for 6 feature sets, analysing single‐parameter and multiparameter models as well as models using recovery vs response‐related features. The highest classification performance (accuracy = 78%) was obtained using the response‐related feature set, including all physiological signals and using GSR‐related features. A worse performance was obtained using single‐signal feature sets based on HR (accuracy = 66%) and ST (accuracy = 59%). Response‐related features outperformed recovery‐related features (accuracy = 63%).ConclusionParticipants with stress‐related complaints may be differentiated from healthy controls by physiological responses to stress tasks. We aimed to bring attention to new exploratory methodologies; further research is needed to validate and replicate the results on larger populations and patients on different areas along the stress continuum.

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Section: Discussionmentioning

confidence: 99%

Comparing task‐induced psychophysiological responses between persons with stress‐related complaints and healthy controls: A methodological pilot study

Smets

Schiavone

Velazquez

et al. 2018

Health Science Reports

View full text Add to dashboard Cite

show abstract

“…However, according to the ranking published [36] , the two most relevant physiological signals for stress detection are: HRV (Heart Rate Variability) and GSR. Various researchers obtained anxiety classification levels exceeding 80% using GSR [67][68][69][70][71][72] . Other recent research considers the HRV (Heart Rate Variability) as a sensitive indicator of stress [35,73,74] and obtained stress detection rates close to 90%, but requires signal processing.…”

Section: Discussionmentioning

confidence: 99%

Computer tool to evaluate the cue reactivity of chemically dependent individuals

Silva

Oliveira

Neto

et al. 2017

Computer Methods and Programs in Biomedicine

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“…Distribution of vigilance processing between one person and another can be assessed through physiological correlates of stress, which in lab research has been studied at the level of the brain (Coan et al, 2006), stress hormones (Kirschbaum, Pirke, & Hellhammer, 1993), and through physiological signal monitoring (Giakoumis, Tzovaras, & Hassapis, 2013). In earlier work, Schuengel, Sterkenburg, Jeczynski, Janssen, and Jongbloed (2009) reported on the buffering effect of a secure relationship for persons with a severe intellectual disability on their stress levels.…”

Section: Research Line 1: Visualizing Distribution Of Vigilance Procementioning

confidence: 99%

Mobile Social Physiology as the Future of Relationship Research and Therapy: Presentation of the Bio-App for Bonding (BAB)

Frederiks¹,

Sterkenburg²,

Lavner³

et al. 2018

Preprint

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Psychological science is in a transitional period: Many findings do not replicate and theories appear not as robust as previously presumed. We suspect that a main reason for theories not appearing as robust is because they are too simple. In this paper, we provide an important step towards this transition in the field of interpersonal relationship research by 1) providing an overarching theoretical framework grounded in existing relationship science, and 2) outlining a novel approach - mobile social physiology – that relies on intelligent technologies like wearable sensors, actuators, and modern analytical methods. At the core of our theoretical principles is co-regulation (one partner’s [statistical] co-dependency on the other partner). Co-regulation has long existed in the literature, but has to date been largely untested. To test the outlined principles, we 3) present a newly programmed app – the Bio-App for Bonding (available on GitHub: https://github.com/co-relab/bioapp). By providing a paradigm shift for relationship research, the field can not only increase the accuracy of measurement and the generalizability of findings, it also allows for moving from the lab to real life situations. We discuss how the mobile social physiology approach is rooted in existing theoretical principles (e.g., Social Baseline and Attachment Theory), extends the concept of co-regulation to allow for specific measurements, and provides a research agenda to develop a model of interpersonal relationships that we hope will stand the test of time.

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Subject-dependent biosignal features for increased accuracy in psychological stress detection

Cited by 40 publications

References 34 publications

Comparing task‐induced psychophysiological responses between persons with stress‐related complaints and healthy controls: A methodological pilot study

Comparing task‐induced psychophysiological responses between persons with stress‐related complaints and healthy controls: A methodological pilot study

Computer tool to evaluate the cue reactivity of chemically dependent individuals

Mobile Social Physiology as the Future of Relationship Research and Therapy: Presentation of the Bio-App for Bonding (BAB)

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