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Anxiety classification in virtual reality using biosensors: A mini scoping review.

Deniz MevlevioğluSabin TabircaDavid Murphy
Published in: PloS one (2023)
Results show that it is possible to create high-accuracy models to determine anxiety in real time. However, it should be noted that there is a lack of standardisation when it comes to defining ground truth for anxiety, making these results difficult to interpret. Additionally, many of these studies included small samples consisting of mostly students, which may bias the results. Future studies should be very careful in defining anxiety and aim for a more inclusive and larger sample. It is also important to research the application of the classification by conducting longitudinal studies.
Keyphrases
  • sleep quality
  • virtual reality
  • machine learning
  • case control
  • deep learning
  • depressive symptoms
  • physical activity