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Open AccessArticle

A Microservices e-Health System for Ecological Frailty Assessment Using Wearables

1
Department of Software Engineering, Computer Sciences School, University of Granada, 18014 Granada, Spain
2
Department of Physiology, Faculty of Health Sciences, University of Granada, 18016 Granada, Spain
*
Author to whom correspondence should be addressed.
This manuscript is an extended version of the conference paper “Designing a Smart Mobile Health System for Ecological Frailty Assessment in Elderly” presented at the 13th International Conference on Ubiquitous Computing and Ambient Intelligence UCAmI 2019, Toledo, Spain, 2–5 December 2019.
Sensors 2020, 20(12), 3427; https://doi.org/10.3390/s20123427
Received: 7 May 2020 / Revised: 13 June 2020 / Accepted: 15 June 2020 / Published: 17 June 2020
(This article belongs to the Special Issue Selected Papers from UCAmI 2019)
The population in developed countries is aging and this fact results in high elderly health costs, as well as a decrease in the number of active working members to support these costs. This could lead to a collapse of the current systems. One of the first insights of the decline in elderly people is frailty, which could be decelerated if it is detected at an early stage. Nowadays, health professionals measure frailty manually through questionnaires and tests of strength or gait focused on the physical dimension. Sensors are increasingly used to measure and monitor different e-health indicators while the user is performing Basic Activities of Daily Life (BADL). In this paper, we present a system based on microservices architecture, which collects sensory data while the older adults perform Instrumental ADLs (IADLs) in combination with BADLs. IADLs involve physical dimension, but also cognitive and social dimensions. With the sensory data we built a machine learning model to assess frailty status which outperforms the previous works that only used BADLs. Our model is accurate, ecological, non-intrusive, flexible and can help health professionals to automatically detect frailty. View Full-Text
Keywords: wearable devices; sensors; mobile health systems; microservices architecture; IoT; machine learning; elderly frailty assessment; e-health wearable devices; sensors; mobile health systems; microservices architecture; IoT; machine learning; elderly frailty assessment; e-health
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Garcia-Moreno, F.M.; Bermudez-Edo, M.; Garrido, J.L.; Rodríguez-García, E.; Pérez-Mármol, J.M.; Rodríguez-Fórtiz, M.J. A Microservices e-Health System for Ecological Frailty Assessment Using Wearables. Sensors 2020, 20, 3427.

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