IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0036556.html
   My bibliography  Save this article

Fall Classification by Machine Learning Using Mobile Phones

Author

Listed:
  • Mark V Albert
  • Konrad Kording
  • Megan Herrmann
  • Arun Jayaraman

Abstract

Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls–left and right lateral, forward trips, and backward slips–while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls.

Suggested Citation

  • Mark V Albert & Konrad Kording & Megan Herrmann & Arun Jayaraman, 2012. "Fall Classification by Machine Learning Using Mobile Phones," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-6, May.
  • Handle: RePEc:plo:pone00:0036556
    DOI: 10.1371/journal.pone.0036556
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0036556
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0036556&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0036556?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Eduardo Casilari & Jose Antonio Santoyo-Ramón & Jose Manuel Cano-García, 2016. "Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-17, December.
    2. Cheng-Wen Lee & Hsiu-Mang Chuang, 2021. "Elderly Fall Detection Devices Using Multiple AIoT Biomedical Sensors," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 11(6), pages 1-1.
    3. Carlos Medrano & Raul Igual & Inmaculada Plaza & Manuel Castro, 2014. "Detecting Falls as Novelties in Acceleration Patterns Acquired with Smartphones," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-9, April.
    4. Chih-Ning Huang & Chia-Tai Chan, 2014. "A ZigBee-Based Location-Aware Fall Detection System for Improving Elderly Telecare," IJERPH, MDPI, vol. 11(4), pages 1-16, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0036556. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.