Affective User Experience (AUX) in Immersive Environments: A Systematic Review of Affective Computing in Immersive Environments for Individuals with Autism Spectrum Disorder (ASD)
Year: 2026
Type: review
Study design: Systematic Review
Abstract: This study examines the integration of affective computing within immersive environments, virtual reality (VR), augmented reality (AR), and mixed reality (MR), to support affective user experience (AUX) in individuals with autism spectrum disorder (ASD). Twenty-eight published empirical studies were analyzed following PRISMA guidelines, focusing on affective modalities, immersive technologies, methodological approaches, and intervention outcomes. Results indicate that immersive systems increasingly incorporate physiological sensing, eye-tracking, behavioral analytics, and, to a lesser extent, facial and speech recognition. Although 89% of studies rely on unimodal affective signals, emerging multimodal frameworks demonstrate enhanced adaptability and real-time emotional responsiveness. VR remains the predominant platform due to its high immersive capacity and controlled manipulation of social stimuli, while AR support interaction in everyday contexts, offering higher accessibility. Across studies, immersive affective systems show consistent benefits in emotion recognition, anxiety reduction, engagement, and social communication. However, the field is limited by small sample sizes, restricted real-world contextual relevance, and a lack of standardized AUX evaluation frameworks. This review identifies methodological gaps and proposes future research directions involving adaptive affective systems, low-cost sensors, and inclusive, longitudinal designs aimed at achieving emotionally intelligent, scalable, and context-aware immersive interventions for people with ASD.
Source: Applied Sciences
DOI: 10.3390/app16031528
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7127410098
