Person Identification and Relapse Detection From Continuous Recordings of Biosignals Challenge: Overview and Results

Abstract

This paper presents an overview of the e-Prevention: Person Identification and Relapse Detection Challenge, which was an open call for researchers at ICASSP-2023. The challenge aimed at the analysis and processing of long-term continuous recordings of biosignals recorded from wearable sensors, namely accelerometers, gyroscopes and heart rate monitors embedded in smartwatches, as well as sleep information and daily step counts, in order to extract high-level representations of the wearer's activity and behavior, termed as digital phenotypes. Specifically, with the goal of analyzing the ability of these digital phenotypes in quantifying behavioral patterns, two tasks were evaluated in two distinct tracks: 1) Identification of the wearer of the smartwatch, and 2) Detection of psychotic relapses in patients in the psychotic spectrum. The long-term data that have been used in this challenge have been acquired during the course of the e-Prevention project (Zlatintsi et al., 2022), an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders. Two baseline systems, one for each task, were described and the validation scores for both tasks were provided to the participants. Herein, we present an overview of the approaches and methods as well as the performance analysis and the results of the 5-top ranked participating teams, which in track 1 achieved accuracy results between 91%-95%, while in track 2 mean PR- and ROC-AUC scores between 0.6051 and 0.6489 were obtained. Finally, we also make the datasets publicly available at https://robotics.ntua.gr/eprevention-sp-challenge/.

Keywords

• Person identification
• relapse detection
• anomaly detection
• autoencoder architectures
• biometric indexes
• deep learning