SP Grand Challenge e-Prevention
Update: For the results of the challenge please read the following paper: https://ieeexplore.ieee.org/abstract/document/10470363
Update ! We publicly release the SPGC e-Prevention data:
Track 1: https://robotics.ntua.gr/wp-content/uploads/SPGC/SPGC_challenge_track_1_release.zip
Track 2: https://robotics.ntua.gr/wp-content/uploads/SPGC/SPGC_challenge_track_2_release.zip
Labels (For both tracks): https://robotics.ntua.gr/wp-content/uploads/SPGC/labels.zip
For all .zip files the username/password are:
Scope of the challenge
The e-Prevention: Person Identification and Relapse Detection from Continuous Recordings of Biosignals challenge has been accepted as a Signal Processing Grand Challenge (SPGC) of the ICASSP 2023 conference！Please refer to https://2023.ieeeicassp.org/call-for-sp-grand-challenges/ for more details.
Abstract/Challenge Overview:
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.
The tasks are of 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 (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.
Challenge duration: December 2022 – February 2023
Contacts
A. Zlatintsi, School of ECE (CVSP / IRAL Group), National Technical Univ. of Athens, nzlat@cs.ntua.gr
P. P. Filntisis, School of ECE (CVSP / IRAL Group), National Technical Univ. of Athens, filby@central.ntua.gr
Reference
Zlatintsi, A.; Filntisis, P.P.; Garoufis, C.; Efthymiou, N.; Maragos, P.; Menychtas, A.; Maglogiannis, I.; Tsanakas, P.; Sounapoglou, T.; Kalisperakis, E.; Karantinos, T.; Lazaridi, M.; Garyfalli, V.; Mantas, A.; Mantonakis, L.; Smyrnis, N. 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. Sensors 2022, 22, 7544. https://doi.org/10.3390/s22197544
Funding: This research has been financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH–CREATE–INNOVATE (project acronym: e-Prevention, code: T1EDK-02890/MIS: 5032797).