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Development of a Strategy to Predict and Detect Falls Using Wearable Sensors

  • Nuno Ferrete RibeiroEmail author
  • João André
  • Lino Costa
  • Cristina P. Santos
Mobile & Wireless Health
  • 68 Downloads
Part of the following topical collections:
  1. Mobile & Wireless Health

Abstract

Falls are a prevalent problem in actual society. Some falls result in injuries and the cost associated with their treatment is high. This is a complex problem that requires several steps in order to be tackled. Firstly, it is crucial to develop strategies that recognize the locomotion mode, indicating the state of the subject in various situations. This article aims to develop a strategy capable of identifying normal gait, the pre-fall condition, and the fall situation, based on a wearable system (IMUs-based). This system was used to collect data from healthy subjects that mimicked falls. The strategy consists, essentially, in the construction and use of classifiers as tools for recognizing the locomotion modes. Two approaches were explored. Associative Skill Memories (ASMs) based classifier and a Convolutional Neural Network (CNN) classifier based on deep learning. Finally, these classifiers were compared, providing for a tool with a good accuracy in recognizing the locomotion modes. Results have shown that the accuracy of the classifiers was quite acceptable. The CNN presented the best results with 92.71% of accuracy considering the pre-fall step different from normal steps, and 100% when not considering.

Keywords

Inertial Measurement Units (IMUs) Gait analysis Principal Component Analysis (PCA) Associative Skill Memories (ASMs) Convolutional Neural Network (CNN) Deep learning 

Notes

Funding

This work has been supported by the FCT - Fundação para a Ciência e Tecnologia - with the scholarship reference PD/BD/141515/2018, by the FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) and P2020 with the Reference Project EML under Grant POCI-01-0247-FEDER-033067, and through the COMPETE 2020 POCI with the Reference Project under Grant POCI-01-0145-FEDER-006941.

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Center for MicroElectroMechanical Systems (CMEMS)University of MinhoGuimarãesPortugal
  2. 2.Production and Systems DepartmentUniversity of MinhoGuimarãesPortugal

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