Although interest in and commitment to one's own health is increasing among the population as a whole, it is often difficult for individuals to implement appropriate measures in their everyday lives. Existing offers are currently often only used by people who already show good health behaviour. The aim of the project is to develop an AI-based learning assistance system that supports healthy everyday behaviour. For this purpose, data from wearables and smartphones will be evaluated in order to provide the user with suggestions for health-promoting behaviour based on situation analyses, behaviour models and digital twins (virtual image of a person). Through sensor-supported observation and modelling of situations and behavioural patterns, a wealth of experience is to be created between users and AI, which can be used as a common communication level. By means of novel, AI-based strategy modelling, individual decision support and recommendations for application scenarios such as healthy nutrition, everyday life and support in old age are to be provided from a large amount of user data. The new assistance system makes it possible to provide personalized, adaptive behavioral recommendations that relate to relevant human experiences and are easy to implement.
Friedrich-Alexander-Universität Erlangen-Nürnberg – Lehrstuhl für Digital Health & Lehrstuhl Digitale Gesellschaft Universität Duisburg-Essen - Institut für Informatik und Wirtschaftsinformatik - paluno - The Ruhr Institute for Software Technology Bodymed AG Interactive Wear AG