Literature DB >> 24271251

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

L Schwickert1, C Becker, U Lindemann, C Maréchal, A Bourke, L Chiari, J L Helbostad, W Zijlstra, K Aminian, C Todd, S Bandinelli, J Klenk.   

Abstract

BACKGROUND AND AIMS: Falls among older people remain a major public health challenge. Body-worn sensors are needed to improve the understanding of the underlying mechanisms and kinematics of falls. The aim of this systematic review is to assemble, extract and critically discuss the information available in published studies, as well as the characteristics of these investigations (fall documentation and technical characteristics).
METHODS: The searching of publically accessible electronic literature databases for articles on fall detection with body-worn sensors identified a collection of 96 records (33 journal articles, 60 conference proceedings and 3 project reports) published between 1998 and 2012. These publications were analysed by two independent expert reviewers. Information was extracted into a custom-built data form and processed using SPSS (SPSS Inc., Chicago, IL, USA).
RESULTS: The main findings were the lack of agreement between the methodology and documentation protocols (study, fall reporting and technical characteristics) used in the studies, as well as a substantial lack of real-world fall recordings. A methodological pitfall identified in most articles was the lack of an established fall definition. The types of sensors and their technical specifications varied considerably between studies.
CONCLUSION: Limited methodological agreement between sensor-based fall detection studies using body-worn sensors was identified. Published evidence-based support for commercially available fall detection devices is still lacking. A worldwide research group consensus is needed to address fundamental issues such as incident verification, the establishment of guidelines for fall reporting and the development of a common fall definition.

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Mesh:

Year:  2013        PMID: 24271251     DOI: 10.1007/s00391-013-0559-8

Source DB:  PubMed          Journal:  Z Gerontol Geriatr        ISSN: 0948-6704            Impact factor:   1.281


  54 in total

1.  Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor.

Authors:  Marie Tolkiehn; Louis Atallah; Benny Lo; Guang-Zhong Yang
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2011

2.  Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm.

Authors:  A K Bourke; J V O'Brien; G M Lyons
Journal:  Gait Posture       Date:  2006-11-13       Impact factor: 2.840

3.  Wearable sensors for reliable fall detection.

Authors:  Jay Chen; Karric Kwong; Dennis Chang; Jerry Luk; Ruzena Bajcsy
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2005

4.  The identification of vertical velocity profiles using an inertial sensor to investigate pre-impact detection of falls.

Authors:  A K Bourke; K J O'Donovan; G Olaighin
Journal:  Med Eng Phys       Date:  2008-02-20       Impact factor: 2.242

5.  Automatic fall detection using wearable biomedical signal measurement terminal.

Authors:  Thuy-Trang Nguyen; Myeong-Chan Cho; Tae-Soo Lee
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2009

Review 6.  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

7.  What videos can tell us about falling.

Authors:  Clemens Becker; Lorenzo Chiari
Journal:  Lancet       Date:  2012-10-17       Impact factor: 79.321

8.  iFall: an Android application for fall monitoring and response.

Authors:  Frank Sposaro; Gary Tyson
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2009

9.  Wireless fall sensor with GPS location for monitoring the elderly.

Authors:  Eric Campo; Etienne Grangereau
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2008

10.  Real-time signal processing of accelerometer data for wearable medical patient monitoring devices.

Authors:  Matt Van Wieringen; J Eklund
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2008
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  35 in total

Review 1.  New developments in the use of positive airway pressure for obstructive sleep apnea.

Authors:  Lucas M Donovan; Schafer Boeder; Atul Malhotra; Sanjay R Patel
Journal:  J Thorac Dis       Date:  2015-08       Impact factor: 2.895

2.  Combining novelty detectors to improve accelerometer-based fall detection.

Authors:  Carlos Medrano; Raúl Igual; Iván García-Magariño; Inmaculada Plaza; Guillermo Azuara
Journal:  Med Biol Eng Comput       Date:  2017-03-01       Impact factor: 2.602

3.  New evidence for gait abnormalities among Parkinson's disease patients who suffer from freezing of gait: insights using a body-fixed sensor worn for 3 days.

Authors:  Aner Weiss; Talia Herman; Nir Giladi; Jeffrey M Hausdorff
Journal:  J Neural Transm (Vienna)       Date:  2014-07-29       Impact factor: 3.575

4.  What Does Big Data Mean for Wearable Sensor Systems? Contribution of the IMIA Wearable Sensors in Healthcare WG.

Authors:  S J Redmond; N H Lovell; G Z Yang; A Horsch; P Lukowicz; L Murrugarra; M Marschollek
Journal:  Yearb Med Inform       Date:  2014-08-15

5.  Reconstruction of body motion during self-reported losses of balance in community-dwelling older adults.

Authors:  Lauro V Ojeda; Peter G Adamczyk; John R Rebula; Linda V Nyquist; Debra M Strasburg; Neil B Alexander
Journal:  Med Eng Phys       Date:  2018-12-20       Impact factor: 2.242

6.  Multimodal sensor-based fall detection within the domestic environment of elderly people.

Authors:  F Feldwieser; M Gietzelt; M Goevercin; M Marschollek; M Meis; S Winkelbach; K H Wolf; J Spehr; E Steinhagen-Thiessen
Journal:  Z Gerontol Geriatr       Date:  2014-08-12       Impact factor: 1.281

Review 7.  Predicting geriatric falls following an episode of emergency department care: a systematic review.

Authors:  Christopher R Carpenter; Michael S Avidan; Tanya Wildes; Susan Stark; Susan A Fowler; Alexander X Lo
Journal:  Acad Emerg Med       Date:  2014-10-07       Impact factor: 3.451

8.  Statistical prediction of load carriage mode and magnitude from inertial sensor derived gait kinematics.

Authors:  Sol Lim; Clive D'Souza
Journal:  Appl Ergon       Date:  2018-11-29       Impact factor: 3.661

9.  Health-Enabling and Ambient Assistive Technologies: Past, Present, Future.

Authors:  R Haux; S Koch; N H Lovell; M Marschollek; N Nakashima; K-H Wolf
Journal:  Yearb Med Inform       Date:  2016-06-30

10.  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

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