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Feature Clustering to Improve Fall Detection: A Preliminary Study

  • Mirko Fáñez
  • José Ramón Villar
  • Enrique de la CalEmail author
  • Víctor M. González
  • Javier Sedano
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 950)

Abstract

In this study, the fall detection method is carried out as stated on [1, 11]; a simple finite state machine is used to process acceleration data in sliding windows and whenever a fall-like event is found, features are extracted from this data. Using some clustering and classification algorithms described here, the event is classified as FALL or NOT_FALL. This research evaluates the performance of different proposed clustering and classification methods. It makes use of a new dataset, with data gathered by a wearable device placed on the wrist and used by several members of the research team and an emergency rescue training manikin under different fall scenarios to simulate the falls. A 10-fold cross-validation is also made to evaluate these methods on unseen data.

Keywords

Fall detection Clustering Classification Wearable devices 

Notes

Acknowledgment

This research has been funded by the Spanish Ministry of Science and Innovation, under project MINECO-TIN2017-84804-R.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mirko Fáñez
    • 1
  • José Ramón Villar
    • 2
  • Enrique de la Cal
    • 2
    Email author
  • Víctor M. González
    • 3
  • Javier Sedano
    • 1
  1. 1.Instituto Tecnológico de Castilla y LeónBurgosSpain
  2. 2.Computer Science Department, EIMEMUniversity of OviedoOviedoSpain
  3. 3.Control and Automatica Department, EPIUniversity of OviedoGijónSpain

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