The challenge will concern the analysis and processing of long-term continuous recordings of biosignals recorded from wearable sensors embedded in smartwatches, in order to extract high-level representations of the wearer’s activity and behavior for two downstream tasks: 1) Identification of the wearer of the smartwatch, and 2) Detection of relapses in patients in the psychotic spectrum. These tasks are of great importance to the biomedical signal processing and psychiatry communities, since through the identification of digital phenotypes from wearable signals, useful insights on the distinctive behavioral patterns and relapse course of patients with psychiatric disorders can be derived, contributing to early symptom identification, and eventually better outcomes of the disorder.
Interested participants are invited to apply their approaches and methods on a large scale dataset acquired through the e-Prevention project [1] (https://eprevention.gr/), including continuous measurements from accelerometers, gyroscopes and heart rate monitors, as well as information about the daily step count and sleep, collected from patients in the psychotic spectrum for a monitoring period of up to 2.5 years, and a control subgroup for a provisional period of 3 months.
Visit the Challenge website for details and more information!
