Literature DB >> 21903502

Evaluation under real-life conditions of a stand-alone fall detector for the elderly subjects.

F Bloch1, V Gautier, N Noury, J-E Lundy, J Poujaud, Y-E Claessens, A-S Rigaud.   

Abstract

BACKGROUND AND OBJECTIVES: Elderly patients unable to get up after a fall or to activate an alarm mechanism are particularly at risk of complications and need to be monitored with extreme care. The different risk factors have fostered the development of stand-alone devices facilitating early detection of falls. We aimed at assessing performance of the Vigi'Fall(®) system, a cutting edge fall detector associating a "passive release" mechanism attached to the patient and including external sensors; in the event of a fall, the system automatically triggers an alarm, and it also incorporates embedded confirmation software. We have put it to the test under real-life conditions so as to evaluate not only its efficacy, but also and more particularly its acceptability and tolerability in elderly subjects.
METHOD: The study ran from March 2007 through December 2008 in a geriatric ward with 10 subjects over 75 years of age, all of whom presented with a risk of falling.
RESULTS: For eight patients wearing an accelerometric sensor, eight "falling" events and 30 "alarm release" events were recorded. Sensitivity and specificity of the device came to 62.5 and 99.5% respectively. For the two patients wearing the complete device, no events were detected. Not a single adverse occurrence was noted. Local tolerance was excellent in all but one of the subjects.
CONCLUSION: Our results clearly show that the device may be worn by patients without discomfort over prolonged periods of time, and also demonstrate that the verification component will help to increase sensitivity in real-life conditions to a level comparable to the level attained in our laboratory studies.
Copyright © 2011 Elsevier Masson SAS. All rights reserved.

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Year:  2011        PMID: 21903502     DOI: 10.1016/j.rehab.2011.07.962

Source DB:  PubMed          Journal:  Ann Phys Rehabil Med        ISSN: 1877-0657


  10 in total

Review 1.  Fall detection with body-worn sensors : a systematic review.

Authors:  L Schwickert; C Becker; U Lindemann; C Maréchal; A Bourke; L Chiari; J L Helbostad; W Zijlstra; K Aminian; C Todd; S Bandinelli; J Klenk
Journal:  Z Gerontol Geriatr       Date:  2013-12       Impact factor: 1.281

Review 2.  Fall detection devices and their use with older adults: a systematic review.

Authors:  Shomir Chaudhuri; Hilaire Thompson; George Demiris
Journal:  J Geriatr Phys Ther       Date:  2014 Oct-Dec       Impact factor: 3.381

3.  Real-World Accuracy and Use of a Wearable Fall Detection Device by Older Adults.

Authors:  Shomir Chaudhuri; Daan Oudejans; Hilaire J Thompson; George Demiris
Journal:  J Am Geriatr Soc       Date:  2015-11       Impact factor: 5.562

4.  A wavelet-based approach to fall detection.

Authors:  Luca Palmerini; Fabio Bagalà; Andrea Zanetti; Jochen Klenk; Clemens Becker; Angelo Cappello
Journal:  Sensors (Basel)       Date:  2015-05-20       Impact factor: 3.576

Review 5.  Involvement of older people in the development of fall detection systems: a scoping review.

Authors:  Friederike J S Thilo; Barbara Hürlimann; Sabine Hahn; Selina Bilger; Jos M G A Schols; Ruud J G Halfens
Journal:  BMC Geriatr       Date:  2016-02-11       Impact factor: 3.921

Review 6.  A Review of Activity Trackers for Senior Citizens: Research Perspectives, Commercial Landscape and the Role of the Insurance Industry.

Authors:  Salvatore Tedesco; John Barton; Brendan O'Flynn
Journal:  Sensors (Basel)       Date:  2017-06-03       Impact factor: 3.576

Review 7.  Methods for the Real-World Evaluation of Fall Detection Technology: A Scoping Review.

Authors:  Robert W Broadley; Jochen Klenk; Sibylle B Thies; Laurence P J Kenney; Malcolm H Granat
Journal:  Sensors (Basel)       Date:  2018-06-27       Impact factor: 3.576

8.  Falls are unintentional: Studying simulations is a waste of faking time.

Authors:  Emma Stack
Journal:  J Rehabil Assist Technol Eng       Date:  2017-10-09

9.  Fall incidents unraveled: a series of 26 video-based real-life fall events in three frail older persons.

Authors:  Ellen Vlaeyen; Mieke Deschodt; Glen Debard; Eddy Dejaeger; Steven Boonen; Toon Goedemé; Bart Vanrumste; Koen Milisen
Journal:  BMC Geriatr       Date:  2013-10-04       Impact factor: 3.921

10.  Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls.

Authors:  Luca Palmerini; Jochen Klenk; Clemens Becker; Lorenzo Chiari
Journal:  Sensors (Basel)       Date:  2020-11-13       Impact factor: 3.576

  10 in total

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