WhoLoDancE (Whole-body interaction Learning for Dance Education, H2020, 2016-2019). WhoLoDancE has delivered a motion capture repository of dance motions, as well as developed and applied machine learning, mixed reality and other breakthrough technologies to dance learning, teaching and choreography, to investigate, preserve and innovate the European dance cultural heritage at the service of dance practitioners, teachers, researchers, choreographers and the interested public.
Cardioproof (Proof of Concept of Model-based Cardiovascular Prediction, FP7, 2014-2017). Cardioproof’s focus has been the integration of multi-level mechanical models of the heart and intervention simulation models for improved clinical decision making. The results have been clinically validated by clinical partners and external stakeholders.
MD-PAEDIGREE (Model-Driven European Paediatric Digital Repository, FP7, 2013 – 2017). MD-PAEDIGREE aimed to support the clinical decision making through a big data multimodal repository of routine patients’ datasets, equipped with advanced data analytics tools and physiopathological models for better and personalised diagnosis and therapeutic approaches.
Avicenna (7FP, 2013-2015). Avicenna was a partnership between biomedical industry and academia for developing technology and standards for predictive computer simulations to contribute to validation methodologies, regulatory policy and networks.
Sim-e-Child (Grid-Enabled Platform for Simulations in Paediatric Cardiology – Toward the Personalized Virtual Child Heart, FP7, 2010-2012). Sim-e-Child developed a grid-enabled platform for large scale simulations in paediatric cardiology, providing a collaborative environment for constructing and validating multi-scale and personalised models of a growing heart and vessels.
Health-e-Child (A grid-enabled pan-Atlantic platform for large scale simulations in paediatric cardiology, FP7, 2006-2010). Health-e-Child developed a platform to integrate information from traditional and emerging sources to support personalised and preventative medicine as well as large-scale, data-based biomedical research and training. The platform collected data about paediatric heart conditions, brain tumours and arthritis.
Lynkeus coordinates the preparation of proposals addressing key areas of EU funding programmes:
Lynkeus proposals so far have been focussed on the development of privacy-enhancing technologies and digital platforms for data sharing, collaborative research and AI development, mostly applied to healthcare research and personalised care (in areas such as paediatrics, cardiology, diabetes, oncology, mental health and multimorbidity), but also XR, motion capture and similarity search applied in cultural heritage (particularly for dance, music, visual arts, gamified learning in special needs education), fashion, structural engineering and much more.