E-Prevention: The ICASSP-2023 Challenge on Person Identification and Relapse Detection from Continuous Recordings of Biosignals

Athanasia Zlatintsi, Panagiotis Paraskevas Filntisis, Niki Efthymiou, Christos Garoufis, George Retsinas, Thomas Sounapoglou, Ilias Maglogiannis, Panayiotis Tsanakas, Nikolaos Smyrnis, Petros Maragos

Abstract: The e-Prevention challenge concerns the analysis and processing of long-term continuous recordings of biosignals recorded from wearable sensors, i.e., accelerometers, gyroscopes and heart rate monitors embedded in smartwatches, as well as sleep information and daily step count, in order to extract high-level representations of the wearer’s activity and behavior, termed as digital phenotypes. The ability of these digital phenotypes to quantify behavioral patterns and traits will be evaluated in two different tasks: 1) Person Identification, and 2) Relapse Detection in patients in the psychotic spectrum. The long-term data that will be used in this challenge have been acquired during the course of the e-Prevention project, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders (i.e, schizophrenia and bipolar disorder). Specifically, the data were continuously collected from patients for a monitoring period of up to 2.5 years, while from the control subgroup for a period of 3 months, constituting one of the largest of its kind ever recorded.