Literature DB >> 24271252

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

J Klenk1, 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.   

Abstract

Objective measurement of real-world fall events by using body-worn sensor devices can improve the understanding of falls in older people and enable new technology to prevent, predict, and automatically recognize falls. However, these events are rare and hence challenging to capture. The FARSEEING (FAll Repository for the design of Smart and sElf-adapaive Environments prolonging INdependent livinG) consortium and associated partners strongly argue that a sufficient dataset of real-world falls can only be acquired through a collaboration of many research groups. Therefore, the major aim of the FARSEEING project is to build a meta-database of real-world falls. To establish this meta-database, standardization of data is necessary to make it possible to combine different sources for analysis and to guarantee data quality. A consensus process was started in January 2012 to propose a standard fall data format, involving 40 experts from different countries and different disciplines working in the field of fall recording and fall prevention. During a web-based Delphi process, possible variables to describe participants, falls, and fall signals were collected and rated by the experts. The summarized results were presented and finally discussed during a workshop at the 20th Conference of the International Society of Posture and Gait Research 2012, in Trondheim, Norway. The consensus includes recommendations for a fall definition, fall reporting (including fall reporting frequency, and fall reporting variables), a minimum clinical dataset, a sensor configuration, and variables to describe the signal characteristics.

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Year:  2013        PMID: 24271252     DOI: 10.1007/s00391-013-0554-0

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


  14 in total

Review 1.  Falls in older people: epidemiology, risk factors and strategies for prevention.

Authors:  Laurence Z Rubenstein
Journal:  Age Ageing       Date:  2006-09       Impact factor: 10.668

Review 2.  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

3.  Epidemiology of falls in residential aged care: analysis of more than 70,000 falls from residents of bavarian nursing homes.

Authors:  Kilian Rapp; Clemens Becker; Ian D Cameron; Hans-Helmut König; Gisela Büchele
Journal:  J Am Med Dir Assoc       Date:  2011-08-04       Impact factor: 4.669

4.  Utilization of the Seniors Falls Investigation Methodology to identify system-wide causes of falls in community-dwelling seniors.

Authors:  Aleksandra A Zecevic; Alan W Salmoni; John H Lewko; Anthoney A Vandervoort; Mark Speechley
Journal:  Gerontologist       Date:  2009-06-12

5.  What videos can tell us about falling.

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

6.  Development of a common outcome data set for fall injury prevention trials: the Prevention of Falls Network Europe consensus.

Authors:  Sarah E Lamb; Ellen C Jørstad-Stein; Klaus Hauer; Clemens Becker
Journal:  J Am Geriatr Soc       Date:  2005-09       Impact factor: 5.562

Review 7.  Wearable systems for monitoring mobility-related activities in older people: a systematic review.

Authors:  Eling D de Bruin; Antonia Hartmann; Daniel Uebelhart; Kurt Murer; Wiebren Zijlstra
Journal:  Clin Rehabil       Date:  2008 Oct-Nov       Impact factor: 3.477

8.  Video capture of the circumstances of falls in elderly people residing in long-term care: an observational study.

Authors:  Stephen N Robinovitch; Fabio Feldman; Yijian Yang; Rebecca Schonnop; Pet Ming Leung; Thiago Sarraf; Joanie Sims-Gould; Marie Loughin
Journal:  Lancet       Date:  2012-10-17       Impact factor: 79.321

9.  Accuracy of patient recall and chart documentation of falls.

Authors:  W A Hale; M J Delaney; T Cable
Journal:  J Am Board Fam Pract       Date:  1993 May-Jun

10.  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

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  9 in total

1.  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

Review 2.  [Prevention of falls and fall-related injuries : Personal balance and future tasks].

Authors:  Clemens Becker
Journal:  Z Gerontol Geriatr       Date:  2017-10-13       Impact factor: 1.281

3.  The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls.

Authors:  Jochen Klenk; Lars Schwickert; Luca Palmerini; Sabato Mellone; Alan Bourke; Espen A F Ihlen; Ngaire Kerse; Klaus Hauer; Mirjam Pijnappels; Matthis Synofzik; Karin Srulijes; Walter Maetzler; Jorunn L Helbostad; Wiebren Zijlstra; Kamiar Aminian; Christopher Todd; Lorenzo Chiari; Clemens Becker
Journal:  Eur Rev Aging Phys Act       Date:  2016-10-30       Impact factor: 3.878

Review 4.  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

Review 5.  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

6.  Detecting falls as novelties in acceleration patterns acquired with smartphones.

Authors:  Carlos Medrano; Raul Igual; Inmaculada Plaza; Manuel Castro
Journal:  PLoS One       Date:  2014-04-15       Impact factor: 3.240

Review 7.  Review of how we should define (and measure) adherence in studies examining older adults' participation in exercise classes.

Authors:  H Hawley-Hague; M Horne; D A Skelton; C Todd
Journal:  BMJ Open       Date:  2016-06-23       Impact factor: 2.692

8.  A Physical Activity Reference Data-Set Recorded from Older Adults Using Body-Worn Inertial Sensors and Video Technology-The ADAPT Study Data-Set.

Authors:  Alan Kevin Bourke; Espen Alexander F Ihlen; Ronny Bergquist; Per Bendik Wik; Beatrix Vereijken; Jorunn L Helbostad
Journal:  Sensors (Basel)       Date:  2017-03-10       Impact factor: 3.576

9.  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

  9 in total

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