Advanced Support System for Treatment Monitoring and Relapse Prevention in Patients with Psychotic Disorders using Long Term Recording and Analysis of Biometric Indexes

Project Details

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).

Overall architecture of the e-Prevention system.

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:

  1. long-term continuous recordings of biometric indexes through simple wearable sensors (i.e., smartwatches),
  2. 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,
  3. parallel studies, medical diagnosis and decisions taken by the psychiatric research group, and
  4. 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.

Visualization of biometric indexes: user movement and heart rate data, i.e., acceleration data, gyroscope data for all three axis (x, y, z-axis) and data collected by the heart rate monitor (i.e., heart rate and RR-interval data).
Example of acceleration signal.
Examples of extracted features for a single user and one day – gray area denotes sleep (first two columns). Last column: sleep-wake ratio and number of steps per day for two weeks of a user. Missing values show periods of time during which HR could not be detected.
Example of the walking diary of user #A – shown are the steps per day, as well as the effort during walking.
Example of the sleeping diary of user #A – the hours per day spent awake and asleep.
Boxplots for features of controls and patients while awake (top row) and asleep (bottom-row). The bold line represents the median, the boxes extend between the 1st and 3rd quartile, whiskers extend to the lowest and highest datum within 1.5 times the inter-quartile range (IQR) of the 1st and 3rd quartile respectively, and outliers are shown as diamonds.

Differences in steps-per-day and sleep-wake ratio for controls, and patients with psychosis.
Work Plan
  • 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 Logo
    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
    Niki Efthymiou
    Panagiotis-Paraskevas Filntisis
    Melina Tziomaka
    Dr. Vrettos Moulos

  • National Technical University of Athens Logo
    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
    Vasia Garyfalli
    MD Manolis Kalisperakis
    MD Makis Mantas
    MD Marina Lazaridi

  • Blockachain Logo
    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
    affordable prices.

    P. I.: Thomas Sounapoglou

    Athanasios Gkoumas
    Evaggelia Mpogiatzi
    Gianna Papaplioura
    Kostas Romanidis



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.

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