A Systematic Review of Experimental Protocols: Towards a Uniform Framework in Virtual Reality Affective Research

Abstract

The integration of affective computing with virtual reality (VR) often uses machine learning to analyze users' emotional responses through physiological and behavioral signals, enabling personalized interactions within VR environments. However, current research in this field is characterized by inconsistent experimental protocols, which hinders comprehensive conclusions and cross-study comparisons. To address this gap, a systematic review was conducted following the PRISMA guidelines, identifying 24 studies that used physiological measures and machine learning to predict emotions in VR settings. The review covers five key areas: experimental protocols, VR environments, implicit measurements, emotion models, and machine learning approaches. In addition, it provides guidelines for standardizing data collection, biosignal processing, and emotion modeling. These proposed guidelines aim to establish consistent reporting practices and experimental protocols, thus improving the comparability and reproducibility of future VR affective computing research.