As Europeans live longer and have fewer children, the proportion of working-age people will fall significantly over the next few decades. This demographic transition is viewed as one of the major challenges to European economies and welfare systems.
FRONT-VL will focus on smart and efficient technical solutions to increase possibilities for the elderly to live at home without being dependent on children or in-home care.
By enabling elderly people to live at home – either independently or assisted – for as long as possible, a good quality of life can be maintained while at the same time drastically reducing care costs.
Based on three use cases I) Rehabilitation, II) Fall Prevention, III) Mental Health, end-user services will be defined and developed to support the end-user with ICT relevant to all stakeholders. This supports the analysis and interpretation of data of individuals and also on a big scale. The use of state-of-the-art machine learning and big data analysis methodologies, together with a profound IoT based data acquisition, will allow the development of sophisticated predictive health related services. FRONT-VL will ensure highest standards of privacy and data ownership of the individual.
The consortium behind FRONT-VL is composed of organizations with long experience in the relevant competence areas needed to realize highly effective end-to-end products which benefit the growing market and societal needs in this area for the coming decades.
The main innovations of FRONT-VL lie in two domains:
First, based on the end-user services for the defined use cases, FRONT-VL aims to create a common service model and a service delivery framework which is able to provide ICT-based home care and health services to the end-users and care professionals in a modular and flexible way. This framework introduces an abstraction layer between the care provider and the end user so that both sides are flexible in the choice of the actual software solution supporting the same service definition language.
Second, automated data collection is utilized to enable peer-to-peer learning and knowledge transfer rather than being used in the mainly one-way fashion by health care professionals of today. Technically, this feedback loop will enhance the quality of services by providing findings based on big data approaches. This also motivates end-users towards a healthier lifestyle by introducing a community aspect.