Selected article for: "accurate model and machine learning"

Author: Garcia-Moreno, Francisco M.; Bermudez-Edo, Maria; Garrido, José Luis; Rodríguez-García, Estefanía; Pérez-Mármol, José Manuel; Rodríguez-Fórtiz, María José
Title: A Microservices e-Health System for Ecological Frailty Assessment Using Wearables â€
  • Cord-id: 7c4iufzg
  • Document date: 2020_6_17
  • ID: 7c4iufzg
    Snippet: 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 ph
    Document: 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.

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