A Microservices e-Health System for Ecological Frailty Assessment Using Wearables
Metadatos
Mostrar el registro completo del ítemAutor
García Moreno, Francisco Manuel; Bermúdez Edo, María del Campo; Garrido Bullejos, José Luis; Rodríguez García, Estefanía; Pérez Mármol, José Manuel; Rodríguez Fórtiz, María JoséEditorial
MDPI
Materia
Wearable devices Sensors Mobile health systems Microservices architecture IoT Machine learning Elderly frailty assessment E-health
Fecha
2020-06-17Referencia bibliográfica
Garcia-Moreno, F. M., Bermudez-Edo, M., Garrido, J. L., Rodríguez-García, E., Pérez-Mármol, J. M., & Rodríguez-Fórtiz, M. J. (2020). A Microservices e-Health System for Ecological Frailty Assessment Using Wearables. Sensors, 20(12), 3427. [doi:10.3390/s20123427]
Patrocinador
Ministry of Economy and Competitiveness from Spain MINECO/FEDER MAT2017-85999P; European Union (EU) MINECO/FEDER MAT2017-85999P; Regional Government of Andalusia Research Fund from Spain A-BIO-157-UGR-18Resumen
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.