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

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

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Article number50
JournalJournal of Medical Systems
Issue number2
Publication statusPublished - 1 Feb 2020


  • Bi-LSTM
  • Elderly surveillance
  • Emergency monitoring
  • Fall detection
  • GRU
  • LSTM

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Information Systems
  • Health Informatics
  • Health Information Management


Dive into the research topics of 'System Design for Emergency Alert Triggered by Falls Using Convolutional Neural Networks'. Together they form a unique fingerprint.

Cite this