  Affective computing has recently gained popularity, especially in the field
of human-computer interaction systems, where effectively evoking and detecting
emotions is of paramount importance to enhance users experience. However,
several issues are hindering progress in the field. In fact, the complexity of
emotions makes it difficult to understand their triggers and control their
elicitation. Additionally, effective emotion recognition requires analyzing
multiple sensor data, such as facial expressions and physiological signals.
These factors combined make it hard to collect high-quality datasets that can
be used for research purposes (e.g., development of emotion recognition
algorithms). Despite these challenges, Virtual Reality (VR) holds promise as a
solution. By providing a controlled and immersive environment, VR enables the
replication of real-world emotional experiences and facilitates the tracking of
signals indicative of emotional states. However, controlling emotion
elicitation remains a challenging task also within VR. This research paper
introduces the Magic Xroom, a VR platform designed to enhance control over
emotion elicitation by leveraging the theory of flow. This theory establishes a
mapping between an individuals skill levels, task difficulty, and perceived
emotions. In the Magic Xroom, the users skill level is continuously assessed,
and task difficulty is adjusted accordingly to evoke specific emotions.
Furthermore, user signals are collected using sensors, and virtual panels are
utilized to determine the ground truth emotional states, making the Magic Xroom
an ideal platform for collecting extensive datasets. The paper provides
detailed implementation information, highlights the main properties of the
Magic Xroom, and presents examples of virtual scenarios to illustrate its
abilities and capabilities.
