System Design for Emergency Alert Triggered by Falls Using Convolutional Neural Networks

Carla Taramasco, Yoslandy Lazo, Tomás Rodenas, Paola Fuentes, Felipe Martínez, Jacques Demongeot

Resultado de la investigación: Contribución a una revistaArtículo

Resumen

The world population ageing is on the rise, which has led to an increase in the demand for medical care due to diseases and symptoms prevalent in health centers. One of the most prevalent symptoms prevalent in older adults is falls, which affect one-third of patients each year and often result in serious injuries that can lead to death. This paper describes the design of a fall detection system for elderly households living alone using very low resolution thermal sensor arrays. The algorithms implemented were LSTM, GRU, and Bi-LSTM; the last one mentioned being that which obtained the best results at 93% in accuracy. The results obtained aim to be a valuable tool for accident prevention for those patients that use it and for clinicians who manage the data.

Idioma originalInglés
Número de artículo50
PublicaciónJournal of Medical Systems
Volumen44
N.º2
DOI
EstadoPublicada - 1 feb 2020

Áreas temáticas de ASJC Scopus

  • Medicina (miscelánea)
  • Sistemas de información
  • Informática aplicada a la salud
  • Gestión de información sanitaria

Huella Profundice en los temas de investigación de 'System Design for Emergency Alert Triggered by Falls Using Convolutional Neural Networks'. En conjunto forman una huella única.

  • Citar esto