Machine Learning Approach to Detect Falls on Elderly People Using Sound

  • Armando Collado-Villaverde
  • María D. R-Moreno
  • David F. Barrero
  • Daniel Rodriguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10350)

Abstract

One of the most notable consequences of aging is the loss of motor function abilities, making elderly people specially susceptible to falls, which is of the most remarkable concerns in elder care. Thus, several solutions have been proposed to detect falls, however, none of them achieved a great success mainly because of the need of wearing a recording device. In this paper, we study the use of sound to detect fall events. The advantage of this approach over the traditional ones is that the subject does not require to wear additional devices to monitor his or her activities. Here, we apply machine learning techniques to process sound simulated the most common type of fall for the elderly, i.e., when the foot collides with an obstacle and the trunk hits the ground before using his/her hands to absorb the fall. The results show that high levels of accuracy can be achieved using only a few signal processing techniques.

Keywords

Fall detection Feature extraction Machine learning Classification Supervised learning Care for the elderly 

Notes

Acknowledgements

The authors thank the contribution of Isabel Pascual Benito, Francisco López Martínez and Helena Hernández Martínez, from Department of Nursing and Physiotherapy of the University of Alcalá, for their help designing and supervising the simulated falls procedure as well as Diego López Pajares and Enrique Alexandre Cortizo for their help regarding the signal processing tasks. This work is supported by UAH (2015/00297/001), JCLM (PEII-2014-015-A) and EphemeCH (TIN2014-56494-C4-4-P) Spanish Ministry of Economy and Competitivity projects.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Armando Collado-Villaverde
    • 1
  • María D. R-Moreno
    • 1
  • David F. Barrero
    • 1
  • Daniel Rodriguez
    • 1
  1. 1.Universidad de Alcalá, Departamento de Automática Crta. Madrid-Barcelona, Alcalá de HenaresMadridSpain

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