Resultados de búsqueda para E-Health

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.

En el presente trabajo se presenta una propuesta tecnológica para eSalud de una arquitectura basada en microservicios, que pueden ser desplegados en dispositivos móviles, wearables y cloud, para evaluar el riesgo de fragilidad de las personas mayores. Dicha propuesta es extensible a otros dominios de problemas del Internet of Things (IoT) dentro del ámbito de la eSalud ya que el patrón de diseño de arquitecturas basadas en microservicios contribuye al desarrollo de sistemas informáticos desacoplados y extensibles. Además, el protocolo Message Queue Telemetry Transport (MQTT) utilizado en IoT favorece el bajo consumo y la no sobrecarga de la red, por lo que incorporar nuevos dispositivos wearables al sistema para recolectar datos de sus sensores se realizaría con poco esfuerzo.