Imagine a digital health coach that predicts when an elderly person might start losing their memory or strength before it happens. It uses a mix of wearable sensors, smart home tech, and medical records to create a 'digital twin' of the patient. This allows doctors to give a personalized care plan to keep people independent and healthy for longer.
Healthcare providers struggle to predict and prevent dementia and frailty, often reacting only after symptoms appear. This leads to higher care costs and lower quality of life for the elderly.
A system for personalized prediction and monitoring using Patient Digital Twins, explainable AI, and integrated health records from sensors and biobanks.
If you are a health-tech app developer dealing with low user engagement in elderly care — this project developed behavioral changing applications and serious games that improve mental health and wellbeing.
If you are a wearable sensor manufacturer dealing with raw data that lacks clinical meaning — this project developed a way to integrate sensor data into Holistic Health Records for personalized prediction of frailty.
If you are a private assisted living provider dealing with reactive rather than proactive care — this project developed AI-based medical devices and virtual assistive technologies to monitor progression of age-related disabilities.
Based on available project data, specific pricing for the resulting tools is not provided, though the project received an EU contribution of EUR 17,591,490 for development.
Yes, the project involves 41 partners across 12 countries and uses Digital Innovation Hubs and Living Labs to facilitate the transition from research to real-world application.
Based on available project data, specific licensing terms are not listed, but the project includes 18 industry partners, suggesting a strong focus on commercial viability.
The project specifies ethical and regulatory requirements to ensure AI-based tools embed ethics by design and comply with healthcare standards.
The project runs from 2024-01-01 to 2027-12-31, with validation occurring across 13 pilot studies.
The consortium is heavily weighted toward commercial application, with a 44% industry ratio comprising 18 companies, 15 of which are SMEs. This high level of industrial involvement, combined with 12 universities and 6 research centers across 12 countries, indicates a strong pipeline for moving the technology from the lab to the market.
Contact the University of Piraeus Research Center in Greece.
Contact SciTransfer to identify the specific SME partners providing the AI-based medical devices.
