Health and Well-being (HW)
Coordinator: Manuel Filipe Vieira Torres Santos
This Research Topic aims to address timely challenges related to Active Ageing, Personalized Health and Assisted Living, Biomedical Informatics and Systems, Improved diagnostic and prognostic biomarkers based on BIG DATA approaches and stimulate the research and contribution of new methodological solutions and its application in realistic clinical and non-clinical contexts. It benefits from the high level of complementarity of the collaborating research groups in all of these fields, and their proven track record at the national and European level in bringing contributions within the scope of this thematic line.
SO1 Active Ageing, personalized health, ambient assisted living, smart interventions
1.1 Personalized Health (PHC) solutions: R&D of innovative algorithms for personalised noninvasive remote monitoring applications (pHealth), able to support the management of well-being, diseases and early diagnosis/prognosis of critical events.
1.2 Digital health to support value-based/sustainable medicine: Value-based healthcare can be broadly defined as a health delivery model in which providers, including hospitals and physicians, are paid based on patient outcomes rather than on number of interventions.
1.3 Ambient assisted living and gamification: Development of new smart and personalized training approaches, helping physical rehabilitation of patients with neurological diseases and human-AI collaboration.
1.4 Smart Interventions Minimally invasive interventions have proven its advantages in different fields (e.g. cardiology, urology, pediatrics).
SO2 Biomedical Informatics Systems
2.1 Clinical decision support systems (cdss): For modern CDSS solutions implementation of guidelines and case-based reasoning approaches no longer suffices.
2.2 Dynamical Systems, physiology and functional Biomarker: Development Dynamical Systems Approaches are very important in the fields of Clinical Neuroscience and Cardiology.
SO3 Brain Computer Interfaces and Medical Robotics
3.1. Brain Computer Interfaces: We aim at boosting reliability and interaction on brain-machine interface systems integrating automatic error-detection approaches.
3.2 Human-robot interaction and Human-Machine Intelligent Cooperation: Learning by demonstration, Medical Robotics and Medical devices for automatic diagnostic of diseases (e.g. Alzheimer’s and Parkinson’s diseases).
SO4 Health ergonomics, occupational safety and security
This will be integrated with partner’s expertise on Ambient Intelligence and e-health applications: physical ergonomics, occupational biomechanics, risk assessment. We will integrate models for multimodal perception, provide assessment tools of the user’s behaviour and human performance, and investigate new human-machine interfaces. This will be applied to the study of the human factors in production systems and physical risk factors.
