A novel monitoring system for fall detection in older people

Carla Taramasco, Tomas Rodenas, Felipe Martinez, Paola Fuentes, Roberto Munoz, Rodrigo Olivares, Victor Hugo C. De Albuquerque, Jacques Demongeot

Research output: Contribution to journalArticlepeer-review

86 Citations (Scopus)


Each year, more than 30% of people over 65 years-old suffer some fall. Unfortunately, this can generate physical and psychological damage, especially if they live alone and they are unable to get help. In this field, several studies have been performed aiming to alert potential falls of the older people by using different types of sensors and algorithms. In this paper, we present a novel non-invasive monitoring system for fall detection in older people who live alone. Our proposal is using very-low-resolution thermal sensors for classifying a fall and then alerting to the care staff. Also, we analyze the performance of three recurrent neural networks for fall detections: Long short-term memory (LSTM), gated recurrent unit, and Bi-LSTM. As many learning algorithms, we have performed a training phase using different test subjects. After several tests, we can observe that the Bi-LSTM approach overcome the others techniques reaching a 93% of accuracy in fall detection. We believe that the bidirectional way of the Bi-LSTM algorithm gives excellent results because the use of their data is influenced by prior and new information, which compares to LSTM and GRU. Information obtained using this system did not compromise the user's privacy, which constitutes an additional advantage of this alternative.

Original languageEnglish
Article number8423052
Pages (from-to)43563-43574
Number of pages12
JournalIEEE Access
Publication statusPublished - 27 Jul 2018


  • artificial neural networks
  • Fall detection
  • older people

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


Dive into the research topics of 'A novel monitoring system for fall detection in older people'. Together they form a unique fingerprint.

Cite this