Conference PaperComputer-Based PTSD Assessment in VR Exposure TherapyTavabi, Leili; Poon, Anna; Rizzo, Albert Skip; Soleymani, MohammadStephanidis, Constantine; Chen, Jessie Y.; Fragomeni, GinoPost-traumatic stress disorder (PTSD) is a mental health condition affecting people who experienced a traumatic event. In addition to the clinical diagnostic criteria for PTSD, behavioral changes in voice, language, facial expression and head movement may occur. In this paper, we demonstrate how a machine learning model trained on a general population with self-reported PTSD scores can be used to provide behavioral metrics that could enhance the accuracy of the clinical diagnosis with patients. Both datasets were collected from a clinical interview conducted by a virtual agent (SimSensei) [10]. The clinical data was recorded from PTSD patients, who were victims of sexual assault, undergoing a VR exposure therapy. A recurrent neural network was trained on verbal, visual and vocal features to recognize PTSD, according to self-reported PCL-C scores [4]. We then performed decision fusion to fuse three modalities to recognize PTSD in patients with a clinical diagnosis, achieving an F1-score of 0.85. Our analysis demonstrates that machine-based PTSD assessment with self-reported PTSD scores can generalize across different groups and be deployed to assist diagnosis of PTSD.2020-10-0810209032HCI International 2020 – Late Breaking Papers: Virtual and Augmented Reality440-449https://doi.org/10.1007/978-3-030-59990-4_321852583National Science Foundation