Editorial
Designing VR for Chronic Pain: Visualizing Autonomic States Through VR Biofeedback to Support Mindfulness
@ARTICLE{10.4108/eetpht.11.11776, author={Sara Khalilipicha and Diane Gromala and Armin Froozanfar and Chris Shaw and Patti Derbyshire}, title={Designing VR for Chronic Pain: Visualizing Autonomic States Through VR Biofeedback to Support Mindfulness}, journal={EAI Endorsed Transactions of Pervasive Health and Technology}, volume={11}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2026}, month={9}, keywords={Virtual reality biofeedback, chronic pain, Electrodermal Activity, Heart Rate Variability, stress classification, machine learning, mindfulness}, doi={10.4108/eetpht.11.11776} }- Sara Khalilipicha
Diane Gromala
Armin Froozanfar
Chris Shaw
Patti Derbyshire
Year: 2026
Designing VR for Chronic Pain: Visualizing Autonomic States Through VR Biofeedback to Support Mindfulness
PHAT
EAI
DOI: 10.4108/eetpht.11.11776
Abstract
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.
Copyright © 2026 Khalilipicha et al., licensed to EAI. This is an open access article distributed under the terms of the (CCBY-NC-SA4.0), which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
