Exploring Physiological Responses in Virtual Reality-based Interventions for Autism Spectrum Disorder: A Data-Driven Investigation
Gianpaolo Alvari, Ersilia Vallefuoco, Melanie Cristofolini, Elio Salvadori, Marco Dianti, Alessia Moltani, Davide Dal Castello, Paola Venuti, Cesare Furlanello
TL;DR
The paper evaluates the feasibility and impact of a therapist-mediated, multiplayer VR serious game for children and adolescents with ASD, augmented by wearable biosignals to capture arousal and autonomic regulation during social tasks. By analyzing behavioral observations alongside HR, HRV, and respiration, the study links physiological markers to social and cognitive responses, identifying age- and sex-related patterns and three distinct session typologies via unsupervised clustering. GLMs reveal specific physiological predictors of social engagement and adaptation, suggesting potential for real-time, personalized VR interventions guided by physiological feedback. Although limited by sample size and data sparsity, findings support the viability of physiology-informed VR therapies for ASD and point to design principles for adaptive, therapist-supported interventions.
Abstract
Virtual Reality (VR) has emerged as a promising tool for enhancing social skills and emotional well-being in individuals with Autism Spectrum Disorder (ASD). Through a technical exploration, this study employs a multiplayer serious gaming environment within VR, engaging 34 individuals diagnosed with ASD and employing high-precision biosensors for a comprehensive view of the participants' arousal and responses during the VR sessions. Participants were subjected to a series of 3 virtual scenarios designed in collaboration with stakeholders and clinical experts to promote socio-cognitive skills and emotional regulation in a controlled and structured virtual environment. We combined the framework with wearable non-invasive sensors for bio-signal acquisition, focusing on the collection of heart rate variability, and respiratory patterns to monitor participants behaviors. Further, behavioral assessments were conducted using observation and semi-structured interviews, with the data analyzed in conjunction with physiological measures to identify correlations and explore digital-intervention efficacy. Preliminary analysis revealed significant correlations between physiological responses and behavioral outcomes, indicating the potential of physiological feedback to enhance VR-based interventions for ASD. The study demonstrated the feasibility of using real-time data to adapt virtual scenarios, suggesting a promising avenue to support personalized therapy. The integration of quantitative physiological feedback into digital platforms represents a forward step in the personalized intervention for ASD. By leveraging real-time data to adjust therapeutic content, this approach promises to enhance the efficacy and engagement of digital-based therapies.