Continuous monitoring of the well-being state of elderly people is about to become an urgent need in the early future due to population aging. Aiming a unified notion of well-being, we find the Intrinsic Capacity concept in accordance with the SMART BEAR project goals. In this study, we mainly focus on the enabling infrastructure, mapping our models to interoperable repositories and to streaming/computing components that can foster monitoring. Our method is also innovative for explicitly combining personalized and risk levels in generating the Intrinsic Capacity score. Leveraging on synthetic data, we represent the outcome trajectories of some sample patients for 1-year continuous monitoring and discuss approaches to characterize them based on the exhibited tendency and evaluate the results from the predictability point of view providing by the entropy of time series concept. At the end, we discuss the possible data quality issues in health care studies using synthetic data.

A methodology to engineering continuous monitoring of intrinsic capacity for elderly people / V. Bellandi, P. Ceravolo, E. Damiani, S. Maghool, M. Cesari, I. Basdekis, E. Iliadou, M.D. Marzan. - In: COMPLEX & INTELLIGENT SYSTEMS. - ISSN 2199-4536. - 8:5(2022 Oct), pp. 3953-3971. [10.1007/s40747-022-00775-w]

A methodology to engineering continuous monitoring of intrinsic capacity for elderly people

V. Bellandi
Primo
;
P. Ceravolo;E. Damiani;S. Maghool
;
M. Cesari;
2022

Abstract

Continuous monitoring of the well-being state of elderly people is about to become an urgent need in the early future due to population aging. Aiming a unified notion of well-being, we find the Intrinsic Capacity concept in accordance with the SMART BEAR project goals. In this study, we mainly focus on the enabling infrastructure, mapping our models to interoperable repositories and to streaming/computing components that can foster monitoring. Our method is also innovative for explicitly combining personalized and risk levels in generating the Intrinsic Capacity score. Leveraging on synthetic data, we represent the outcome trajectories of some sample patients for 1-year continuous monitoring and discuss approaches to characterize them based on the exhibited tendency and evaluate the results from the predictability point of view providing by the entropy of time series concept. At the end, we discuss the possible data quality issues in health care studies using synthetic data.
Big data; IoT; Intrinsic capacity
Settore INF/01 - Informatica
H20_RIA19EDAMI_02 - Smart Big Data Platform to Offer Evidence-based Personalised Support for Healthy and Independent Living at Home (SMART BEAR) - DAMIANI, ERNESTO - H20_RIA - Horizon 2020_Research & Innovation Action/Innovation Action - 2019
1-giu-2022
Article (author)
File in questo prodotto:
File Dimensione Formato  
Bellandi2022_Article_AMethodologyToEngineeringConti.pdf

accesso aperto

Descrizione: online first
Tipologia: Publisher's version/PDF
Dimensione 1.54 MB
Formato Adobe PDF
1.54 MB Adobe PDF Visualizza/Apri
s40747-022-00775-w.pdf

accesso aperto

Tipologia: Publisher's version/PDF
Dimensione 1.53 MB
Formato Adobe PDF
1.53 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/930443
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 0
social impact