E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures - Zlatintsi, Athanasia; Filntisis, Panagiotis P.; Garoufis, Christos; Efthymiou, Niki; Maragos, Petros; Menychtas, Andreas; Maglogiannis, Ilias; Tsanakas, Panayiotis; Sounapoglou, Thomas; Kalisperakis, Emmanouil; Karantinos, Thomas; Lazaridi, Marina; et al. (Sensors, 2022)
Wearable technologies and digital phenotyping foster unique opportunities for designing novel intelligent electronic services that can address various well-being issues in patients with mental disorders (i.e., schizophrenia and bipolar disorder), thus having the potential to revolutionize psychiatry and its clinical practice. In this paper, we present e-Prevention, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders. The technologies offered through e-Prevention include: (i) long-term continuous recording of biometric and behavioral indices through a smartwatch; (ii) video recordings of patients while being interviewed by a clinician, using a tablet; (iii) automatic and systematic storage of these data in a dedicated Cloud server and; (iv) the ability of relapse detection and prediction. This paper focuses on the description of the e-Prevention system and the methodologies developed for the identification of feature representations that correlate with and can predict psychopathology and relapses in patients with mental disorders. Specifically, we tackle the problem of relapse detection and prediction using Machine and Deep Learning techniques on all collected data. The results are promising, indicating that such predictions could be made and leading eventually to the prediction of psychopathology and the prevention of relapses.
Keywords: anomaly detection; autoencoder architectures; biometric indexes; deep learning; digital phenotyping; facial expressions; psychotic disorders; relapse detection; spontaneous speech; wearable technologies