METHODS article
Front. Virtual Real.
Sec. Virtual Reality and Human Behaviour
Volume 6 - 2025 | doi: 10.3389/frvir.2025.1589744
This article is part of the Research TopicEnabling EnvironmentsView all articles
Ethical Methodologies for Digital Identity Privacy in AI-driven Dance Movement Therapy as a Preventative Mental Health Mechanism in the Extended Reality
Provisionally accepted
- University of Oxford, Oxford, United Kingdom
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This study applies a hybrid methodology combining a systematic review and an AIenhanced pilot study to explore the correlation between physical activity and episodic-paroxysmal anxiety (EPA) within Extended Reality (XR) environments. The pilot study uses a multimodal biometric approach (incorporating accelerometry, heart rate variability (HRV), and skin conductance) integrated with AI-driven pattern recognition algorithms to measure the real-time physiological impact of Dance Movement Therapy (DMT). By establishing a feedback loop between physical activity and anxiety-related biomarkers, the study presents a dynamic framework for non-pharmacological mental health intervention design. The emerging methodologies for AI-driven Preventative Mechanisms, are tested with a pilot study, consisting of a cohort of 20 participants, exploring the correlation between physical activity and anxiety levels through advanced biometric measures such as accelerometers, skin conductance, and heart rate variability. The key findings reveal that Dance Movement Therapy within Extended Reality environments significantly reduces anxiety levels in individuals with episodic-paroxysmal anxiety, as evidenced by measurable improvements in biometric indicators such as heart rate variability and skin conductance.
Keywords: XR (Extended Reality), Virtual reality (VR), Artificial intelligence (AI), Mental Health, non-pharmacological interventions, Biometric technologies, emotion analysis, Digital Identity Privacy
Received: 07 Mar 2025; Accepted: 30 Jun 2025.
Copyright: © 2025 Radanliev. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Petar Radanliev, University of Oxford, Oxford, United Kingdom
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