Personalized therapy, in which a therapeutic practice is adapted to an individual patient, can lead to improved health outcomes. Typically, this is accomplished by relying on a therapist's training and intuition along with feedback from a patient. However, this requires the therapist to become an expert on any technological components, such as in the case of Virtual Reality Exposure Therapy (VRET). While there exist approaches to automatically adapt therapeutic content to a patient, they generally rely on hand-authored, pre-defined rules, which may not generalize to all individuals. In this paper, we propose an approach to automatically adapt therapeutic content to patients based on physiological measures. We implement our approach in the context of virtual reality arachnophobia exposure therapy, and rely on experience-driven procedural content generation via reinforcement learning (ED-PCGRL) to generate virtual spiders to match an individual patient. Through a human subject study, we demonstrate that our system significantly outperforms a more common rules-based method, highlighting its potential for enhancing personalized therapeutic interventions.

Personalized virtual reality exposure therapy is a therapeutic practice that can adapt to an individual patient, leading to better health outcomes. Measuring a patient's mental state to adjust the therapy is a critical but difficult task. Most published studies use subjective methods to estimate a patient's mental state, which can be inaccurate. This article proposes a virtual reality exposure therapy (VRET) platform capable of assessing a patient's mental state using non-intrusive and widely available physiological signals such as photoplethysmography (PPG). In a case study, we evaluate how PPG signals can be used to detect two binary classifications: peaceful and stressful states. Sixteen healthy subjects were exposed to the two VR environments (relaxed and stressful). Using LOSO cross-validation, our best classification model could predict the two states with a 70.6% accuracy which outperforms many more complex approaches.

The World Health Organisation (WHO) states that: "There is no health without mental health". Health population studies show that the most common mental disorders are anxiety disorders. Nowadays, Virtual Reality Exposure Therapy (VRET) is used to help people manage their anxiety. The next step forward, is personalisation of VRET to further improve therapy and patient motivation. The effects of VRET would even be more increased by automating this personalisation by taking background and data from wearables into account. In the ongoing PATRONUS project, we aim at designing such a system that provides truly personalised VRET. In light of this project, this paper discusses the current shortcomings of Contextual Multi-Armed Bandits and related challenges in personalisation. Future research areas are proposed, namely the use of semantics in reinforcement learning and Contextual Multi-Armed Bandits for personalisation as well as clustering patients based on background information in order to train better models.