Type: dissertation
Abstract: Traditional methods for diagnosing depression, such as clinical interviews and self-reported questionnaires, often suffer from bias and inaccuracies, which can result in misdiagnosis and inadequate treatment. This research endeavors to provide a more objective and precise approach to mental health diagnostics, particularly through the use of psychophysiological measurements and VR. Study 1 focused on the relationship between depressive symptoms, heart rate variability, and cognitive performance, emphasizing the role of autonomic nervous system functioning. Study 2 examined symptom-specific alterations in emotional processing, identifying distinct patterns related to cognitive-affective and somatic symptoms. Study 3 explored the impact of depressive symptoms on time perception, revealing a blunted emotional modulation in individuals with depression. Study 4 introduced a VR-based serious game, demonstrating the feasibility of using machine learning and immersive VR environments for depression detection. Together, these findings contribute to the development of more personalized and objective tools for assessing depression, laying the foundation for future research in the field.
Author: MURA, FRANCESCA
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6991107412