ArticleSensors (Basel, Switzerland)2020
Integrating Biosignals Measurement in Virtual Reality Environments for Anxiety Detection.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence (AI) for virtual reality exposure therapy (VRET): A systematic review.Translational psychiatry · 2026Pooled it
- Artificial Intelligence-Integrated Virtual Reality in Mental Health Care: A Scoping Review of Evidence, Clinical Applications, and Future Directions.Journal of clinical medicine · 2026Review
- Work at Heights Training: Conventional Approach with and Without Immersive Virtual Reality Study Protocol.Methods and protocols · 2026Article
- Wearable devices for anxiety assessment: a systematic review.Communications medicine · 2026Article
- Article
- Can reinforcement learning effectively prevent depression relapse?World journal of psychiatry · 2025Review
- Virtual stressors with real impact: what virtual reality-based biobehavioral research can teach us about typical and atypical stress responsivity.Translational psychiatry · 2024Review
- Real-Time Classification of Anxiety in Virtual Reality Therapy Using Biosensors and a Convolutional Neural Network.Biosensors · 2024Article
- Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions.Frontiers in psychiatry · 2024Article
- Article
- Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework.Sensors (Basel, Switzerland) · 2022Article
- Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics.Biosensors · 2022Article
- Targeted Memory Reactivation During REM Sleep in Patients With Social Anxiety Disorder.Frontiers in psychiatry · 2022Article
- Cognitive behavioral therapy with adaptive virtual reality exposure vs. cognitive behavioral therapy withFrontiers in psychiatry · 2022Article
- Wearable Sensor-based Multimodal Physiological Responses of Socially Anxious Individuals in Social Contexts on Zoom.IEEE transactions on affective computingArticle
- Decoding anxiety: A scoping review of observable cues.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
This paper proposes a protocol for the acquisition and processing of biophysical signals in virtual reality applications, particularly in phobia therapy experiments. This protocol aims to ensure that the measurement and processing phases are performed effectively, to obtain clean data that can be used to estimate the users' anxiety levels. The protocol has been designed after analyzing the experimental data of seven subjects who have been exposed to heights in a virtual reality environment. The subjects' level of anxiety has been estimated based on the real-time evaluation of a nonlinear function that has as parameters various features extracted from the biophysical signals. The highest classification accuracy was obtained using a combination of seven heart rate and electrodermal activity features in the time domain and frequency domain.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.