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

BACKGROUND: Real-world fall events objectively measured by body-worn sensors can improve the understanding of fall events in older people. However, these events are rare and hence challenging to capture. Therefore, the FARSEEING (FAll Repository for the design of Smart and sElf-adaptive Environments...

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Autores principales: Klenk, Jochen, Schwickert, Lars, Palmerini, Luca, Mellone, Sabato, Bourke, Alan, Ihlen, Espen A. F., Kerse, Ngaire, Hauer, Klaus, Pijnappels, Mirjam, Synofzik, Matthis, Srulijes, Karin, Maetzler, Walter, Helbostad, Jorunn L., Zijlstra, Wiebren, Aminian, Kamiar, Todd, Christopher, Chiari, Lorenzo, Becker, Clemens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5086409/
https://www.ncbi.nlm.nih.gov/pubmed/27807468
http://dx.doi.org/10.1186/s11556-016-0168-9
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author Klenk, Jochen
Schwickert, Lars
Palmerini, Luca
Mellone, Sabato
Bourke, Alan
Ihlen, Espen A. F.
Kerse, Ngaire
Hauer, Klaus
Pijnappels, Mirjam
Synofzik, Matthis
Srulijes, Karin
Maetzler, Walter
Helbostad, Jorunn L.
Zijlstra, Wiebren
Aminian, Kamiar
Todd, Christopher
Chiari, Lorenzo
Becker, Clemens
author_facet Klenk, Jochen
Schwickert, Lars
Palmerini, Luca
Mellone, Sabato
Bourke, Alan
Ihlen, Espen A. F.
Kerse, Ngaire
Hauer, Klaus
Pijnappels, Mirjam
Synofzik, Matthis
Srulijes, Karin
Maetzler, Walter
Helbostad, Jorunn L.
Zijlstra, Wiebren
Aminian, Kamiar
Todd, Christopher
Chiari, Lorenzo
Becker, Clemens
author_sort Klenk, Jochen
collection PubMed
description BACKGROUND: Real-world fall events objectively measured by body-worn sensors can improve the understanding of fall events in older people. However, these events are rare and hence challenging to capture. Therefore, the FARSEEING (FAll Repository for the design of Smart and sElf-adaptive Environments prolonging Independent livinG) consortium and associated partners started to build up a meta-database of real-world falls. RESULTS: Between January 2012 and December 2015 more than 300 real-world fall events have been recorded. This is currently the largest collection of real-world fall data recorded with inertial sensors. A signal processing and fall verification procedure has been developed and applied to the data. Since the end of 2015, 208 verified real-world fall events are available for analyses. The fall events have been recorded within several studies, with different methods, and in different populations. All sensor signals include at least accelerometer measurements and 58 % additionally include gyroscope and magnetometer measurements. The collection of data is ongoing and open to further partners contributing with fall signals. The FARSEEING consortium also aims to share the collected real-world falls data with other researchers on request. CONCLUSIONS: The FARSEEING meta-database will help to improve the understanding of falls and enable new approaches in fall risk assessment, fall prevention, and fall detection in both aging and disease. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s11556-016-0168-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-50864092016-11-02 The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls Klenk, Jochen Schwickert, Lars Palmerini, Luca Mellone, Sabato Bourke, Alan Ihlen, Espen A. F. Kerse, Ngaire Hauer, Klaus Pijnappels, Mirjam Synofzik, Matthis Srulijes, Karin Maetzler, Walter Helbostad, Jorunn L. Zijlstra, Wiebren Aminian, Kamiar Todd, Christopher Chiari, Lorenzo Becker, Clemens Eur Rev Aging Phys Act Research Article BACKGROUND: Real-world fall events objectively measured by body-worn sensors can improve the understanding of fall events in older people. However, these events are rare and hence challenging to capture. Therefore, the FARSEEING (FAll Repository for the design of Smart and sElf-adaptive Environments prolonging Independent livinG) consortium and associated partners started to build up a meta-database of real-world falls. RESULTS: Between January 2012 and December 2015 more than 300 real-world fall events have been recorded. This is currently the largest collection of real-world fall data recorded with inertial sensors. A signal processing and fall verification procedure has been developed and applied to the data. Since the end of 2015, 208 verified real-world fall events are available for analyses. The fall events have been recorded within several studies, with different methods, and in different populations. All sensor signals include at least accelerometer measurements and 58 % additionally include gyroscope and magnetometer measurements. The collection of data is ongoing and open to further partners contributing with fall signals. The FARSEEING consortium also aims to share the collected real-world falls data with other researchers on request. CONCLUSIONS: The FARSEEING meta-database will help to improve the understanding of falls and enable new approaches in fall risk assessment, fall prevention, and fall detection in both aging and disease. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s11556-016-0168-9) contains supplementary material, which is available to authorized users. BioMed Central 2016-10-30 /pmc/articles/PMC5086409/ /pubmed/27807468 http://dx.doi.org/10.1186/s11556-016-0168-9 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Klenk, Jochen
Schwickert, Lars
Palmerini, Luca
Mellone, Sabato
Bourke, Alan
Ihlen, Espen A. F.
Kerse, Ngaire
Hauer, Klaus
Pijnappels, Mirjam
Synofzik, Matthis
Srulijes, Karin
Maetzler, Walter
Helbostad, Jorunn L.
Zijlstra, Wiebren
Aminian, Kamiar
Todd, Christopher
Chiari, Lorenzo
Becker, Clemens
The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls
title The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls
title_full The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls
title_fullStr The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls
title_full_unstemmed The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls
title_short The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls
title_sort farseeing real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5086409/
https://www.ncbi.nlm.nih.gov/pubmed/27807468
http://dx.doi.org/10.1186/s11556-016-0168-9
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