Detail projektu

Development of an integrated concept for the deployment of innovative technologies and services allowing independent living of frail elderly (niCE-life)

Období řešení: 01.07.2019 — 30.06.2022

O projektu

The niCE-life project aims to foster social inclusion and care coordination of frail elderly with focus on persons withcognitive medium/low deficits including Alzheimer's and Parkinson's diseases and other chronic diseases through development of transnationally applicable model of health and care services for frail elderly (based on e-Care Network developed in Bologna, IT) by using progressive key enabling technologies (i.e. sensor technologies, ICT and data analysis techniques) to prevent frailty, enhance quality of care and support their independent living, social contacts and assistance continuity after hospital discharges. The intelligent monitoring platform, new health and care solutions and organizational changes of care practice will be designed and tested in pilot actions and complemented by local action plans taking into account national health and social care systems and local conditions. Together with targeted trainings they will contribute to strengthening capacities and competencies of public authorities and health and care providers to efficiently address pressing social challenges and foster independent living of frail elderly, reduce risk factors caused by frailty and enhance social integration of frail persons. Innovative character of project is underpinned by the introduction of state-of-the-art technologies in care services in partner regions and artificial-intelligence-based monitoring platform for caregivers and patients.

Klíčová slova
e-health; artificial intelligence; big data

Označení

CE1581

Originální jazyk

angličtina

Řešitelé

Útvary

Ústav telekomunikací
- příjemce (01.01.2019 - 31.12.2021)

Zdroje financování

Evropská unie - Interreg CENTRAL EUROPE 2014-2020

- plně financující (2019-11-11 - 2022-12-31)

Výsledky

KOLAŘÍK, M.; BURGET, R.; ŘÍHA, K. Comparing Normalization Methods for Limited Batch Size Segmentation Neural Networks. In 2020 43rd International Conference on Telecommunications and Signal Processing (TSP). 2020. p. 677-680. ISBN: 978-1-7281-6376-5.
Detail

Joshi, R.C., Yadav, S., Pathak, V.K., Malhotra, H.S., Khokhar, H.V.S., Parihar, A., Kohli, N., Himanshu, D., Garg, R.K., Bhatt, M.L.B. and Kumar, R. A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images. BIOCYBERN BIOMED ENG, 2021, vol. 41, no. 1, p. 1-16. ISSN: 0208-5216.
Detail

RAJNOHA, M.; MEZINA, A.; BURGET, R. Multi-Frame Labeled Faces Database: Towards Face Super-Resolution from Realistic Video Sequences. Applied Sciences - Basel, 2020, vol. 10, no. 20, p. 1-27. ISSN: 2076-3417.
Detail

Baghela, N;Dutta, M. K.;Burget, R. Automatic diagnosis of multiple cardiac diseases from PCG signals using convolutional neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2020, vol. 197, no. 12, p. 1-11. ISSN: 0169-2607.
Detail