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

Francisco M Garcia-Moreno,Maria Bermudez-Edo,José Luis Garrido,José Manuel Pérez-Mármol,Estefanía Rodríguez-García,María José Rodríguez-Fórtiz

doi:10.3390/s20123427

Francisco M Garcia-Moreno, Maria Bermudez-Edo + Show 4 more

Open Access

https://doi.org/10.3390/s20123427

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Abstract

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.

Highlights

In a global ageing society, it is important to detect the frailty in the early stages to be able to slow down the decline of elderly people and to keep them active and healthy for as long as possible
In order to validate the technological solution, older adults were assessed in a real environment system; (2) our system requires little time of use and it is transparent for older adults, because they
The participants recruited for the proof of concept only have to wear the smartwatch; (3) our system is easy to use for older adults, they do not require were five adults coming from a community centerdoes in Granada three of themthewere any technological competence; and (4) our system not require(Spain); extra personnel to train userswomen

Summary

Introduction

In a global ageing society, it is important to detect the frailty in the early stages to be able to slow down the decline of elderly people and to keep them active and healthy for as long as possible. Frailty is a syndrome of older adults that increases the risk of falls, hospitalizations and even death [1,2]; affecting. 11% of the home-dwelling elderly population without hospitalization and increasing drastically to. The early detection of frailty has been proved to increase the independence of the older adults and the decrease on health care costs [4,5]. The current detection of frailty is performed manually via independent tests of strength, gait or self-report questionnaires, being Fried [4] the most used test, which only assess the physical dimension of frailty. The tests are time consuming, and require investment in health resources and physical interaction between patients and doctors. The detection and manual assessment of the frailty for all Sensors 2020, 20, 3427; doi:10.3390/s20123427 www.mdpi.com/journal/sensors

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