# A Personalised Emotion-Based Model for Relaxation in Virtual Reality
## 2 Related Work  
VR has been used in clinical settings to treat a range of cognitive, emotional and motor problems in various psychological and psychiatric disorders [9; 11; 12; 13; 14; 15; 16]. Many studies have shown that the VR-based therapy is as effective as their in-vivo (traditional) counterpart [13; 17; 18].  
[PERSON] et al. [19], [PERSON] et al. [20], [PERSON] et al. [21] and [PERSON] et al. [22] performed studies providing empirical evidence for the effect of using VR for reducing stress in various domains of application. Indeed, the study by [PERSON] et al. reported positive results on stress reduction and/or anxiety through exposure to natural imagery in VR. Additionally, the authors employed abdominal breathing and muscle relaxation as well as psycho-education in their study. Their tests were focused on people with mood disorders who had been submitted to a hospital. [PERSON] et al. restricted themselves to patients in an Intensive Care Unit (ICU) while [PERSON] et al. explored the effect on people living and working in isolated and confined environments (e.g., submarines, space). Whereas [PERSON] et al. did not envision a particular target group but focused on underwater scene imagery. Additionally, [PERSON] et al. discussed the need for a personalised experience in relaxation VR. They suggest that the effect of natural imagery on stress reduction depends on personal preference. Many of the existing work focuses on (personalised) relaxation in the context of a specific condition (e.g., cancer treatment, ICU) and not for general relaxation or de-stressing. This work focuses on the latter.  
It has been suggested in previous research that providing a personalised treatment would indeed improve the therapy, either in effectiveness or in user satisfaction [23]. In 1987, [PERSON] and [PERSON] [24] already did a comparative study between two relaxation strategies. Their finding was that both strategies were equally effective while one was more preferred than the other by patients. [PERSON] et al. [25] came to similar conclusions as well when performing a study comparing six different relaxation techniques. This shows there is a definite and clear need for personalised relaxation therapy. However, most studies on VR based relaxation lack this personalised and adaptive approach [26]. Only a few papers in literature make contributions to this specific domain. In their paper [10], [PERSON] et al. propose a VR experience with personalised content as an alternative to generic story-telling or guided activities for regulating emotions in VR.  
Personalised systems need input to adapt to the user. For real-time adaptations, continuous data streams are preferred to estimate a certain state of the user, e.g., predicting the emotional state for biofeedback. Substantial research has been done on detecting this emotional state from various sensor data [27; 28]. [PERSON] et al. [29] developed a system for predicting stress while driving based on heart rate and skin conductance data. [PERSON] et al. [30] also used heart rate but in combination with spontaneous blink rate to detect anxiety. They did, however, find that heart rate was the most prominent predictor.  
Other work researches the use of machine learning techniques such as Convolutional Neural Network (CNN) [31] and Support Vector Machine (SVM) [32] in detecting emotions. These algorithms are often very complex and need large amounts of computing power and expensive wearables. In some cases, they are designed for one particular setting without proof for application to other settings [29]. For many use cases, aimed at relaxation, these are unacceptable requirements. These use cases require simple usage, non-intrusive and non-obstructing sensors, and preferably cheap hardware. Therefore, there is a need for simpler models predicting the emotional state of a person. Developing such a model needs a deep understanding of emotions on a biological and physiological level [33].