Co‐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 code:T1EDK-02890).
About the Project
The goal of the e-Prevention project is to develop innovative and advanced remote electronic services for medical support that will facilitate effective treatment monitoring and relapse prevention in patients with psychotic disorders (i.e., bipolar disorder and schizophrenia).
e-Prevention will develop a novel intelligent system which will offer the possibility for timely diagnosis of psychotic symptom’s relapses and adverse medicine side effects by combining:
- long-term continuous recordings of biometric indexes through simple wearable sensors (i.e., smartwatches),
- a portable device (tablet) that is used to record short-term audio-visual videos of the patient while communicating with the clinical personnel on weekly basis,
- parallel studies, medical diagnosis and decisions taken by the psychiatric research group, and
- development of an intelligent data processing and recognition system, which will be based on Cloud computing and processing of large-scale (big) data, providing statistical measurements, detections and estimates of changes and patterns that will facilitate the prediction of clinical symptoms and side effects of the patient’s medication.
- WP1: Data collection from healthy volunteers and patients. Patient Monitoring-Treatment. Model Evaluation
- WP2: Monitoring sensors and cloud computing infrastructure
- WP3: Multimodal data processing for recognition of changes and trends
- WP4: Development of the integrated e-Prevention System
- WP5: Commercial exploitation of results
Areas / Keywords
Psychotic relapse, prevention of psychotic episode, antipsychotic medication, mood stabilized induced tremor, schizophrenia, bipolar disorder.
Signal processing, pattern recognition and machine learning, computer vision and image processing, multimodal human-computer interaction, multi-sensory processing, parallel and distributed processing, cloud systems/computer architecture, biomedical information systems.
National Technical University of Athens
School of Electrical and Computer Engineering, Intelligent Robotics
and Automation Laboratory
P.I. & Scientific Director: Prof. Petros Maragos
Prof. Panagiotis Tsanakas
Prof. Ilias Maglogiannis (Dept. of Digital Systems, Univ. of
Assoc. Prof Gerasimos Potamianos (Dept. of ECE, Univ. of
Dr. Athanasia Zlatintsi
Dr. Georgios Retsinas
Dr. Andreas Menychtas
Dr. Dimitra Georgiou
Dr. Vrettos Moulos
University Mental Health, Neurosciences and Precision Medicine Research
Institute “COSTAS STEFANIS”
Laboratory of Cognitive Neuroscience and Sensoriomotor Control
P. I.: Prof Nikolaos Smyrnis (School of Medicine, National
Kapodistrian Univ. Athens)
Dr. Thomas Karantinos
MD Manolis Kalisperakis
MD Makis Mantas
MD Marina Lazaridi
Blockachain, a custom software engineering company, operating as a
full-service software development company. Blockachain exploits modern
design principles, along with the latest blockchain, cloud, mobile and
desktop technologies to deliver software of best-in-class performance at
P. I.: Thomas Sounapoglou
- Smartwatch digital phenotypes predict positive and negative symptom variation in a longitudinal monitoring study of patients with psychotic disorders, Frontiers in Psychiatry (Submitted)
- Relapse Prediction from long-term Wearable Data using Self-Supervised Learning and Survival-Analysis, ICASSP-2023 (Submitted)
- Employing Digital Phenotype Identification for the Discovery of Psychotic Relapses, ICASSP-2023 (Submitted)
- e-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analysing Long-Term Multimodal Data from Wearables and Video Captures, Sensors, Special Issue “AI for Biomedical Sensing and Imaging” 22(19), 7544, Oct. 2022. [DOI.https://doi.org/10.3390/s22197544.
- A Comparative Study of Autoencoder Architectures for Mental Health Analysis using Wearable Sensors Data, EUSIPCO–22
- Towards Unsupervised Subject-Independent Speech-Based Relapse Detextion in Patients with Psychosis using Variational Autoencoders, EUSIPCO–22
- Can a smartwatch help cognitive neuroscience? the relation between physical activity and cognitive performance in healthy young adults, SfN Global Connectome, Society for Neuroscience
- Smartwatch digital phenotypes relate to positive and negative psychotic symptom variation in a longitudinal monitoring study (e-Prevention) of patients with psychosis, Neuroscience 2021 50th annual meeting (SfN)
- An Unsupervised Learning Approach for Detecting Relapses from Spontaneous Speech in Patients with Psychosis, BHI-2021
- Evaluating mental patients utilizing video analysis of facial expressions, AIAI-2021
- Sparsity in Max-Plus Algebra and Applications in Multivariate Convex, ICASSP 2021
- Advances in Morphological Neural Networks: Training, Pruning and Enforcing Shape Constraints, ICASSP-2021
- An intelligent cloud-based platform for effective monitoring of patients with psychotic disorders, AIAI-2020
- Person Identification Using Deep Convolutional Neural Networks On Short-term Signals From Wearable Sensors, ICASSP-2020
- Tropical Modeling Of Weighted Transducer Algorithms On Graphs, ICASSP-2019
Presentation of achieved results at conferences and workshops
- 12-17 May 2019: NTUA presented e-Prevention results in ICASSP 2019.
- 04-08 May 2020: NTUA presented e-Prevention results in ICASSP 2020.
- 05-07 June 2020: NTUA presented e-Prevention results in AIAI2020.
- 17-20 June 2021: NTUA presented e-Prevention results in AIAI2021.
- 06-11 June 2021: NTUA presented e-Prevention results in ICASSP 2021.
- 27-30 July 2021: NTUA presented e-Prevention results in BHI 2021.
- 08-11 November 2021: UMHRI presented e-Prevention results in SfN 2021.
- 29 Aug-02 Sep 2022: NTUA presented e-Prevention results in EUSIPCO 2022.