Fall detection with orientation calibration using a single motion sensor

Shuo Yu, Hsinchun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Falls are a major threat for senior citizens living independently. Sensor technologies and fall detection algorithms have emerged as a reliable, low-cost solution for this issue. We proposed a sensor orientation calibration algorithm to better address the uncertainty issue faced by fall detection algorithms in real world applications. We conducted controlled experiments of simulated fall events and non-fall activities on student subjects. We evaluated our proposed algorithm using sequence matching based machine learning approaches on five different body positions. The algorithm achieved an F-measure of 90 to 95% in detecting falls. Sensors worn as necklace pendants or in chest pockets performed best.

Original languageEnglish (US)
Title of host publicationWireless Mobile Communication and Healthcare - 6th International Conference, MobiHealth 2016, Proceedings
PublisherSpringer Verlag
Pages233-240
Number of pages8
Volume192
ISBN (Print)9783319588766
DOIs
StatePublished - 2017
Event6th International Conference on Wireless Mobile Communication and Healthcare, MobiHealth 2016 - Milan, Italy
Duration: Nov 14 2016Nov 16 2016

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume192
ISSN (Print)1867-8211

Other

Other6th International Conference on Wireless Mobile Communication and Healthcare, MobiHealth 2016
CountryItaly
CityMilan
Period11/14/1611/16/16

Keywords

  • Fall detection
  • Machine learning
  • Sensor orientation calibration

ASJC Scopus subject areas

  • Computer Networks and Communications

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  • Cite this

    Yu, S., & Chen, H. (2017). Fall detection with orientation calibration using a single motion sensor. In Wireless Mobile Communication and Healthcare - 6th International Conference, MobiHealth 2016, Proceedings (Vol. 192, pp. 233-240). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 192). Springer Verlag. https://doi.org/10.1007/978-3-319-58877-3_31