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. 2020 Jun 17;20(12):3427.
doi: 10.3390/s20123427.

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

Francisco M Garcia-Moreno  1 , Maria Bermudez-Edo  1 , José Luis Garrido  1 , Estefanía Rodríguez-García  2 , José Manuel Pérez-Mármol  2 , María José Rodríguez-Fórtiz  1
Affiliations

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

Francisco M Garcia-Moreno et al. Sensors (Basel). .

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.

Keywords: IoT; e-health; elderly frailty assessment; machine learning; microservices architecture; mobile health systems; sensors; wearable devices.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Microservices architecture taxonomy.
Figure 2
Figure 2
Microservices architecture for frailty assessment.
Figure 3
Figure 3
Workflow communication details of the microservice architecture for frailty assessment.
Figure 4
Figure 4
Data analysis pipeline for frailty status assessment.
Figure 5
Figure 5
Comparison between frail and non-frail individuals by heart rate [24].
Figure 6
Figure 6
Performance of different machine learning algorithms by RFE embedded feature selection.
Figure 7
Figure 7
Performance of 1-NN by RFE embedded feature selection over shopping phases. Walking: (1) walking to the supermarket; (2) coming back. Sitting/Standing: (1) sitting; (2) standing; (3) standing at start point; (4) and sitting back. Shopping: (1) in the supermarket; (2) looking for the product to purchase; (3) picking the product; (4) going to the checkout; (5) in the checkout; (6) paying; (7) go to the exit; (8) in the outside. Packed Shopping: all phases of Shopping but packed in only one phase.
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