Eghi – Enhanced good health intelligence for personal behavioural strategies in everyday life

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Although interest in personal health has risen in the population within the past years, it is often difficult for people to integrate dedicated routines into daily life. Existing solutions are mostly used by people who pay attention to their health anyway.

Goals and Approaches

Within the Eghi project, we aim at developing an AI-based, self-learning assistance system, which enhances healthy behaviours in everyday life. To achieve this goal, data from wearables and smartphones are analysed to generate health-improving recommendations for the user based on situation analysis, behaviour models and digital twins. Using sensor-based monitoring and modelling of situations and behaviour patterns, users and AI may interchange experiences. AI-based strategic modelling based on high amounts of user data shall generate individual decision assistance and recommendations for several scenarios, including nutrition, daily-life activity, as well as assistance at the higher ages.

Innovations and Perspectives 

With the novel assistance system, it will be possible to give feasible, personalised and adaptive behaviour recommendations, which are related to relevant experiences the user has.


Eghi is funded by the Bundesministerium für Bildung und Forschung (BMBF).

Project Website


David Kopyto, Rui Zhang, Oliver Amft, "Audio-Based Onset Detection applied to Chewing Cycle Segmentation", ACM International Symposium on Wearable Computers, Association for Computing Machinery, September 21, 2021.
David Kopyto, Lena Uhlenberg, Rui Zhang, Valeska Stonawski, Stefanie Horndasch, Oliver Amft, "AIM in Eating Disorders", Artificial Intelligence in Medicine, Springer International Publishing, 2021.



      David Kopyto

      • Job title: Researcher
      • Address:
        Henkestraße 91, Haus 7, 1. OG
        91052 Erlangen
      • Phone number: +49 9131 85-23608
      • Email:

      Friedrich-Alexander-Universität Erlangen-Nürnberg