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Detail projektu
Období řešení: 01.07.2019 — 30.06.2022
Zdroje financování
Evropská unie - Interreg CENTRAL EUROPE 2014-2020
- plně financující (2019-11-11 - 2022-12-31)
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 slovae-health; artificial intelligence; big data
Označení
CE1581
Originální jazyk
angličtina
Řešitelé
Burget Radim, doc. Ing., Ph.D. - hlavní řešitelBrezany Peter, Univ. Prof. Dr. - spoluřešitelGaláž Zoltán, Ing., Ph.D. - spoluřešitelMekyska Jiří, doc. Ing., Ph.D. - spoluřešitel
Útvary
Ústav telekomunikací- příjemce (01.01.2019 - 31.12.2021)
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
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
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
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
MIKULEC, M.; GALÁŽ, Z.; MEKYSKA, J.; MUCHA, J.; BRABENEC, L.; MORÁVKOVÁ, I.; REKTOROVÁ, I. Prodromal Diagnosis of Lewy Body Diseases Based on Actigraphy. In 2022 45th International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2022. p. 403-406. ISBN: 978-1-6654-6948-7.Detail
MIKULEC, M. Identification Of Sleep/Wake Stages In Actigraphy Data Utilising Gradient Boosting Algorithm. In Proceedings II of the 27st Conference STUDENT EEICT 2021 selected papers. 1. Brno: Brno University of Technology, Faculty of Electrical Engineering and Communication, 2021. p. 270-274. ISBN: 978-80-214-5943-4.Detail
MIKULEC, M.; MEKYSKA, J.; SIGMUND, J.; GALÁŽ, Z.; BRABENEC, L.; MORÁVKOVÁ, I.; REKTOROVÁ, I. Automatic Segmentation of Actigraphy Data Utilising Gradient Boosting Algorithm. In 2021 44th International Conference on Telecommunications and Signal Processing. Brno, Czech Republic: IEEE, 2021. p. 399-402. ISBN: 978-1-6654-2933-7.Detail
KOLAŘÍK, M.; BURGET, R.; ŘÍHA, K.; BARTUŠEK, K. Suitability of CT and MRI Imaging for Automatic Spine Segmentation Using Deep Learning. In 2021 44th International Conference on Telecommunications and Signal Processing (TSP). NEW YORK: IEEE, 2021. p. 390-393. ISBN: 978-1-6654-2934-4.Detail
MIKULEC, M.; MEKYSKA, J.; GÁLÁŽ Z. Parkinson’s Disease Recognition based on Sleep Metrics from Actigraphy and Sleep Diaries. In Proceedings II of the 28th Conference STUDENT EEICT 2022 Selected papers. 1. Brno, Czech Republic: Brno University of Technology, Faculty of Electronic Engineering and Communication, 2022. p. 281-285. ISBN: 978-80-214-6030-0.Detail
MIKULEC, M.; GALÁŽ, Z.; MEKYSKA, J.; BURGET, R.: Sleep analysis system; Sleep analysis system. https://github.com/BDALab/sleep-analysis-system. URL: https://github.com/BDALab/sleep-analysis-system. (software)Detail