Designing VR for Chronic Pain: Visualizing Autonomic States Through VR Biofeedback to Support Mindfulness
DOI:
https://doi.org/10.4108/eetpht.11.11776Keywords:
Virtual reality biofeedback, chronic pain, Electrodermal Activity, Heart Rate Variability, stress classification, machine learning, mindfulnessAbstract
Chronic pain affects physical functioning and psychological well-being, reducing quality of life. This paper presents the Virtual Meditative Walk, a closed-loop VR biofeedback environment that supports mindfulness-based chronic pain management through indirect, in-world visualization of autonomic state. To improve adaptation, we integrate a machine learning-based physiological inference pipeline using Electrodermal Activity and Heart Rate Variability. Using the combined WESAD and StressID dataset, the final Extra Trees model improved over the original VMW feedback method in the cross-sample binary setting, reaching 91.77%binary cross-sample accuracy and 86.05% multiclass cross-sample accuracy. Leave-one-subject-out validation was lower, showing that subject-independent stress inference remains challenging. These findings support machine learning as a promising but limited method for adaptive VR biofeedback in chronic pain contexts.
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