Literature DB >> 22169389

Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects.

M Kangas1, I Vikman, L Nyberg, R Korpelainen, J Lindblom, T Jämsä.   

Abstract

Falling is a common accident among older people. Automatic fall detectors are one method of improving security. However, in most cases, fall detectors are designed and tested with data from experimental falls in younger people. This study is one of the first to provide fall-related acceleration data obtained from real-life falls. Wireless sensors were used to collect acceleration data during a six-month test period in older people. Data from five events representing forward falls, a sideways fall, a backwards fall, and a fall out of bed were collected and compared with experimental falls performed by middle-aged test subjects. The signals from real-life falls had similar features to those from intentional falls. Real-life forward, sideways and backward falls all showed a pre impact phase and an impact phase that were in keeping with the model that was based on experimental falls. In addition, the fall out of bed had a similar acceleration profile as the experimental falls of the same type. However, there were differences in the parameters that were used for the detection of the fall phases. The beginning of the fall was detected in all of the real-life falls starting from a standing posture, whereas the high pre impact velocity was not. In some real-life falls, multiple impacts suggested protective actions. In conclusion, this study demonstrated similarities between real-life falls of older people and experimental falls of middle-aged subjects. However, some fall characteristics detected from experimental falls were not detectable in acceleration signals from corresponding heterogeneous real-life falls. Copyright Â
© 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 22169389     DOI: 10.1016/j.gaitpost.2011.11.016

Source DB:  PubMed          Journal:  Gait Posture        ISSN: 0966-6362            Impact factor:   2.840


  32 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.  Proposal for a multiphase fall model based on real-world fall recordings with body-fixed sensors.

Authors:  C Becker; L Schwickert; S Mellone; F Bagalà; L Chiari; J L Helbostad; W Zijlstra; K Aminian; A Bourke; C Todd; S Bandinelli; N Kerse; J Klenk
Journal:  Z Gerontol Geriatr       Date:  2012-12       Impact factor: 1.281

3.  GAL@Home: a feasibility study of sensor-based in-home fall detection.

Authors:  M Gietzelt; J Spehr; Y Ehmen; S Wegel; F Feldwieser; M Meis; M Marschollek; K-H Wolf; E Steinhagen-Thiessen; M Gövercin
Journal:  Z Gerontol Geriatr       Date:  2012-12       Impact factor: 1.281

Review 4.  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

5.  Bridging the gap between real-life data and simulated data by providing a highly realistic fall dataset for evaluating camera-based fall detection algorithms.

Authors:  Greet Baldewijns; Glen Debard; Gert Mertes; Bart Vanrumste; Tom Croonenborghs
Journal:  Healthc Technol Lett       Date:  2016-03-21

6.  Development of a standard fall data format for signals from body-worn sensors : the FARSEEING consensus.

Authors:  J Klenk; L Chiari; J L Helbostad; W Zijlstra; K Aminian; C Todd; S Bandinelli; N Kerse; L Schwickert; S Mellone; F Bagalá; K Delbaere; K Hauer; S J Redmond; S Robinovitch; O Aziz; M Schwenk; A Zecevic; T Zieschang; C Becker
Journal:  Z Gerontol Geriatr       Date:  2013-12       Impact factor: 1.281

7.  Evaluation of accelerometer-based fall detection algorithms on real-world falls.

Authors:  Fabio Bagalà; Clemens Becker; Angelo Cappello; Lorenzo Chiari; Kamiar Aminian; Jeffrey M Hausdorff; Wiebren Zijlstra; Jochen Klenk
Journal:  PLoS One       Date:  2012-05-16       Impact factor: 3.240

8.  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

9.  Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms.

Authors:  Goran Šeketa; Lovro Pavlaković; Dominik Džaja; Igor Lacković; Ratko Magjarević
Journal:  Sensors (Basel)       Date:  2021-06-24       Impact factor: 3.576

Review 10.  Analysis of Android Device-Based Solutions for Fall Detection.

Authors:  Eduardo Casilari; Rafael Luque; María-José Morón
Journal:  Sensors (Basel)       Date:  2015-07-23       Impact factor: 3.576

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