Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

Chien Pin Liu*, Ju Hsuan Li, En Ping Chu, Chia Yeh Hsieh, Kai Chun Liu, Chia Tai Chan, Yu Tsao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a 'long-lie.' Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD.

Original languageEnglish
Title of host publication2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665493840
DOIs
StatePublished - 2023
Event2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023 - Jeju, Korea, Republic of
Duration: 14 Jun 202316 Jun 2023

Publication series

Name2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023 - Conference Proceedings

Conference

Conference2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023
Country/TerritoryKorea, Republic of
CityJeju
Period14/06/2316/06/23

Keywords

  • deep learning
  • ensemble learning
  • fall detection
  • sensor applications

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