This paper researches the classification of human emotions in a virtual reality (VR) context by analysing psychophysiological signals and facial expressions. Key objectives include exploring emotion categorisation models, identifying critical human signals for assessing emotions, and evaluating the accuracy of these signals in VR environments. A systematic literature review was performed through peer-reviewed articles, forming the basis for our methodologies. The integration of various emotion classifiers employs a ‘late fusion’ technique due to varying accuracies among classifiers. Notably, facial expression analysis faces challenges from VR equipment occluding crucial facial regions like the eyes, which significantly impacts emotion recognition accuracy. A weighted averaging system prioritises the psychophysiological classifier over the facial recognition classifiers due to its higher accuracy. Findings suggest that while combined techniques are promising, they struggle with mixed emotional states as well as with fear and trust emotions. The research underscores the potential and limitations of current technologies, recommending enhanced algorithms for effective interpretation of complex emotional expressions in VR. The study provides a groundwork for future advancements, aiming to refine emotion recognition systems through systematic data collection and algorithm optimisation.