Neuro-Fuzzy Physiological Computing to Assess Stress Levels in Virtual Reality Therapy

Tartarisco, Gennaro; Carbonaro, Nicola; Tonacci, Alessandro; Bernava, Giuseppe Massimo; Arnao, Antonino Andrea; Crifaci, Giulia; Cipresso, Pietro; Riva, Giuseppe; Gaggioli, Andrea; Rossi, Danilo De; Tognetti, Alessandro; Pioggia, Giovanni

doi:10.1093/iwc/iwv010

Cited by 22 publications

(19 citation statements)

References 59 publications

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“…Looking at the signals with which stress was attempted to be classified, it is noticeable that each approach measures the cardiovascular activity. Either with optical sensors on the finger (Cho et al, 2017;Ham et al, 2017) or with electrical sensors (Tartarisco et al, 2015;Robitaille and McGuffin, 2019;Ishaque et al, 2020). Additional measures that were used by these studies are EDA (Cho et al, 2017;Ishaque et al, 2020), skin temperature (Cho et al, 2017), respiration Ishaque et al (2020), or motion activity (Tartarisco et al, 2015;Robitaille and McGuffin, 2019).…”

Section: Stressmentioning

confidence: 99%

“…In an even simpler setup, with only one finger-worn PPG sensor and a Linear Discriminant Analysis, Ham et al (2017) achieved an accuracy of approximately 80% for three different classes. Tartarisco et al (2015) took an approach with a wearable chest band. They collected ECG, respiration, and motion data and trained a neuro-fuzzy neural network that achieved an accuracy of 83% for four different classes.…”

Section: Stressmentioning

confidence: 99%

“…Individual electrodes distributed over the body could be bothersome. Approaches with more comfortable chest bands showed somewhat worse accuracy values, yet were able to effectively classify different levels of stress (Tartarisco et al, 2015;Robitaille and McGuffin, 2019). Future research could aim on improving the quality of stress indicators in VR based on unobtrusive sensors.…”

Section: Stressmentioning

confidence: 99%

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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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Section: Stressmentioning

confidence: 99%

Section: Stressmentioning

confidence: 99%

Section: Stressmentioning

confidence: 99%

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

Halbig

Latoschik

2021

Front. Virtual Real.

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“…The priority of the human-machine interface (HMI) simulation experiment is to challenge the participants' problemsolving ability in a similar manner to real-world HMI situations. In contrast to human-human interfaces, human participants from the HMI demonstrate lower emotional intensity over less diverse feelings [1], [20]. Therefore, the simulation should be able to introduce adequate adjustable stimuli.…”

Section: A Human-machine Interface Simulationmentioning

confidence: 99%

An Adaptive General Type-2 Fuzzy Logic Approach for Psychophysiological State Modeling in Real-Time Human–Machine Interfaces

Mahfouf

Torres-Salomao³

2021

IEEE Trans. Human-Mach. Syst.

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“…Psychophysiological correlates reflect elicited emotions, and they represent variations in the central and peripheral nervous system. Thus, they must be considered in more complex models rather than only univariate analysis to identify the effect of a stressor more accurately [ 9 , 29 , 30 , 31 , 32 ]. With this consideration in mind, we provided evidence of the effectiveness of the two acute stressors (Stroop Task and Arithmetic Task) by means of computational analyses.…”

Section: Introductionmentioning

confidence: 99%

Computational Psychometrics Using Psychophysiological Measures for the Assessment of Acute Mental Stress

Cipresso

Colombo

Riva

2019

Sensors

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The goal of this study was to provide reliable quantitative analyses of psycho-physiological measures during acute mental stress. Acute, time-limited stressors are used extensively as experimental stimuli in psychophysiological research. In particular, the Stroop Color Word Task and the Arithmetical Task have been widely used in several settings as effective mental stressors. We collected psychophysiological data on blood volume pulse, thoracic respiration, and skin conductance from 60 participants at rest and during stressful situations. Subsequently, we used statistical univariate tests and multivariate computational approaches to conduct comprehensive studies on the discriminative properties of each condition in relation to psychophysiological correlates. The results showed evidence of a greater discrimination capability of the Arithmetical Task compared to the Stroop test. The best predictors were the short time Heart Rate Variability (HRV) indices, in particular, the Respiratory Sinus Arrhythmia index, which in turn could be predicted by other HRV and respiratory indices in a hierarchical, multi-level regression analysis. Thus, computational psychometrics analyses proved to be an effective tool for studying such complex variables. They could represent the first step in developing complex platforms for the automatic detection of mental stress, which could improve the treatment.

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Neuro-Fuzzy Physiological Computing to Assess Stress Levels in Virtual Reality Therapy

Cited by 22 publications

References 59 publications

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

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

An Adaptive General Type-2 Fuzzy Logic Approach for Psychophysiological State Modeling in Real-Time Human–Machine Interfaces

Computational Psychometrics Using Psychophysiological Measures for the Assessment of Acute Mental Stress

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