Towards effective detection of elderly falls with CNN-LSTM neural networks

Enol García, Mario Villar, Mirko Fáñez, José R. Villar, Enrique de la Cal, Sung Bae Cho

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Fall detection is a very challenging task that has a clear impact in the autonomous living of the elderly individuals: suffering a fall with no support increases the fears of the elderly population to continue living by themselves. This study proposes the use of a non-invasive tri-axial accelerometer device placed on a wrist to measure the movements of the participant. The novelty of this study is two fold: on the one hand, the use of a Long-Short Term Memory Neural Network (LSTM) for classification of the Time Series and, on the other hand, the proposal of a novel data augmentation stage that introduces variability in the training by merging the Time Series gathered from both human activities of daily living. The experimentation shows that the combination of a LSTM model together with the data augmentation produces more robust and accurate models that perfectly cope with the validation stage; the high impact fall event detection can be considered solved.

Original languageEnglish
Pages (from-to)231-240
Number of pages10
Publication statusPublished - 2022 Aug 21

Bibliographical note

Funding Information:
This research has been funded by the Spanish Ministry of Science and Innovation under project MINECO-TIN2017-84804-R, PID2020-112726RB-I00 and the State Research Agency (AEI, Spain) under grant agreement No RED2018-102312-T (IA-Biomed). Moreover, this work was also partly supported by IITP grant funded by the Korean government (MSIT) (No. 2020-0-01361, AI Graduate School Program (Yonsei University)).

Publisher Copyright:
© 2022 The Author(s)

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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