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

Abstract

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.

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Correspondence to Carla Taramasco.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional Committee on Bioethics of Research in Humans of the University of Valparaiso and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Taramasco, C., Lazo, Y., Rodenas, T. et al. System Design for Emergency Alert Triggered by Falls Using Convolutional Neural Networks. J Med Syst 44, 50 (2020). https://doi.org/10.1007/s10916-019-1484-1

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Keywords

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