Ziel
Transition from healthy status to Parkinson’s Disease (PD) is vaguely tractable, since symptoms can be so subtle in the early stages that they go unnoticed. Lack of biomarkers and/or findings on routine MRI and CT scans, PD is left undiagnosed for years, gradually affecting the life of over 6.5 million of older adults (>55-60 yrs) worldwide, increasing the risk of their health deterioration. Epidemiological studies conclude that early intervention could have an inverse relation with the PD-related risks of progressive frailty, falls and emotional shift towards depression. Based on this evidence, the cardinal objective of i-PROGNOSIS is the development of (i) an ICT-based behavioural analysis approach for capturing, as early as possible, the PD symptoms appearance, and (ii) the application of ICT-based interventions countering identified risks. To achieve this, awareness initiatives will be employed, so as to construct i-PROGNOSIS community, targeting > 5000 older individuals within the duration of the project, in order to unobtrusively sense large scale behavioural data from its members, acquired from their natural use of mobile devices (smartphone/smartwatch). Ensuring anonymisation and secure Cloud archiving, i-PROGNOSIS will develop and employ advanced big data analytics and machine learning techniques, in a distributed and privacy aware fashion, so as to instantiate a PD Behavioural Model and construct reliable early PD symptoms detection alarms. To those identified and clinically validated as early stage PD patients, ICT-based interventions will be provided via the i-PROGNOSIS Intervention Platform, including: a) a Personalised Game Suite (ExerGames, DietaryGames, EmoGames, Handwriting/VoiceGames) for physical/emotional support, b) targeted nocturnal intervention to increase relaxation/sleep quality and c) assistive interventions for voice enhancement and gait rhythm guidance. In this way, i-PROGNOSIS will constructively contribute to active and healthy ageing.
Wissenschaftliches Gebiet
CORDIS klassifiziert Projekte mit EuroSciVoc, einer mehrsprachigen Taxonomie der Wissenschaftsbereiche, durch einen halbautomatischen Prozess, der auf Verfahren der Verarbeitung natürlicher Sprache beruht.
CORDIS klassifiziert Projekte mit EuroSciVoc, einer mehrsprachigen Taxonomie der Wissenschaftsbereiche, durch einen halbautomatischen Prozess, der auf Verfahren der Verarbeitung natürlicher Sprache beruht.
- engineering and technologymedical engineeringdiagnostic imagingcomputed tomography
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsmobile phones
- engineering and technologymedical engineeringdiagnostic imagingmagnetic resonance imaging
- medical and health sciencesbasic medicineneurologyparkinson
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Schlüsselbegriffe
Programm/Programme
Thema/Themen
Aufforderung zur Vorschlagseinreichung
Andere Projekte für diesen Aufruf anzeigenUnterauftrag
H2020-PHC-2015-single-stage
Finanzierungsplan
RIA - Research and Innovation actionKoordinator
546 36 THESSALONIKI
Griechenland