Lifestyle-related early detection and interventions

Big Data Analytics techniques will be exploited to address risk stratification and early detection, based on lifestyles analysis including: pattern recognition for the improvement of public health surveillance and for the early detection of cognitive decline and frailty; data mining for inductive reasoning and exploratory data analysis; Cluster Analysis for identifying high-risk groups among elder citizens. In the above cases timely intervention is provided by through AI-based, digital coaches developed e.g. on top of Samsung AI assistant, Bixby through Natural Language Processing techniques, based structured conversations, consultation and education.

Target population:

Lifestyle-related use case targets low strata of the risk pyramid (healthy population or population with low risk) across all sites implementing this use case. In this sense, all sites target general elderly population at risk of chronic diseases and/or mental impairment.

Key enabling technologies:

  • App for smartphone or tablet, enabling healthy lifestyle promotion as well as regular follow up of citizens through their interaction with a digital coach (all sites). 
  • Citizen’s online platform, both acting as a data hub and allowing a friendlier interphase for answering to questionnaires (Milton Keynes, Poland) 
  • Professional’s online platform/dashboard, providing overview of alarm signs and/or relevant information (Aragon, Saxony) 
  • Wearables/medical devices, intended to monitor and track key variables such as physical activity, blood pressure, weight or real-time adherence to treatment (Basque country, Greece, Poland). 
  • Home sensors/robots, to explore the citizen’s home for risks while collecting information on daily activities and potentially providing some direct support (Milton Keynes) A key requirement will be that all these KETs, and the data flows associated with them, comply with all the regulatory standards and reliable, safe and robust