A novel and accurate emotion recognition system for real-time and continuous patient monitoring in psychiatry

Informazioni relative al progetto

ADAPTE

ID dell’accordo di sovvenzione: 190129251

DOI

10.3030/190129251

Progetto chiuso

Data della firma CE 19 Ottobre 2023

Data di avvio 1 Novembre 2023

Data di completamento 30 Aprile 2026

Finanziato da

• The European Innovation Council (EIC)

Costo totale

€ 3 568 155,00

Contributo UE

€ 2 497 708,00

Coordinato da

CEPHALGO
France

Periodic Reporting for period 1 - ADAPTE (A novel and accurate emotion recognition system for real-time and continuous patient monitoring in psychiatry)

Periodo di rendicontazione: 2023-11-01 al 2025-04-30

Sintesi del contesto e degli obiettivi generali del progetto

Mental healthcare support faces three critical challenges: low treatment adherence (>60% dropout rate), difficulty in tracking emotional states between clinical visits, and insufficient between-session support. Current methods rely heavily on patient self-reporting and memory, leading to potential gaps in care continuity.
ADAPTE aims to enhance mental healthcare support through an AI-powered patient-centric software solution that:

- Facilitates emotion tracking through natural speech analysis
- Supports patient engagement and adherence
- Bridges communication gaps between clinical sessions
- Provides objective insights to supplement clinical care

The project initially focuses on supporting individuals with Major Depressive Disorder (MDD), with potential expansion to other mental health conditions. Expected impact includes:

- Making emotional wellbeing support accessible to >95% through smartphone-based tools
- Supporting treatment adherence improvement to 75% (from current 40%)
- Enhancing patient-clinician communication

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

Technical Developments:

- Enhanced speech-based emotion recognition through advanced AI
- Developed robust quality assurance processes
- Created personalised engagement frameworks

Clinical Validation:

- Established partnerships with psychiatric hospitals covering 6,000 MDD patients
- Successfully tested patient conversational pathway v1.0
- Validated patient engagement modules with clinicians
- Implemented secure data management protocols

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

Technical Innovation:

- Novel application of time-series AI analysis to speech patterns
- Integrated emotion recognition with patient engagement tools
- Developed scalable cloud-based architecture for widespread deployment

Market Impact:

- First-to-market patient-centric solution combining continuous monitoring with engagement
- 95% accessibility through smartphone-based approach
- Demonstrated feasibility of continuous emotion monitoring in real-world settings

Next Steps:

- Enhance AI models with larger datasets
- Ensure product-market fit with stakeholders’ feedback
- Iterate patient-centric software with early adopters
- Create sustainable business model