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

Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elderly. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Fabio Bagalà, Clemens Becker, Angelo Cappello, Lorenzo Chiari, Kamiar Aminian, Jeffrey M Hausdorff, Wiebren Zijlstra, Jochen Klenk
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b5945b4b3dc04fe591c32b8a96f30168
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:b5945b4b3dc04fe591c32b8a96f30168
record_format	dspace
spelling	oai:doaj.org-article:b5945b4b3dc04fe591c32b8a96f301682021-11-18T07:18:31ZEvaluation of accelerometer-based fall detection algorithms on real-world falls.1932-620310.1371/journal.pone.0037062https://doaj.org/article/b5945b4b3dc04fe591c32b8a96f301682012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22615890/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elderly. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing some of the negative consequences of falls. Many different approaches have been explored to automatically detect a fall using inertial sensors. Although previously published algorithms report high sensitivity (SE) and high specificity (SP), they have usually been tested on simulated falls performed by healthy volunteers. We recently collected acceleration data during a number of real-world falls among a patient population with a high-fall-risk as part of the SensAction-AAL European project. The aim of the present study is to benchmark the performance of thirteen published fall-detection algorithms when they are applied to the database of 29 real-world falls. To the best of our knowledge, this is the first systematic comparison of fall detection algorithms tested on real-world falls. We found that the SP average of the thirteen algorithms, was (mean ± std) 83.0% ± 30.3% (maximum value = 98%). The SE was considerably lower (SE = 57.0% ± 27.3%, maximum value = 82.8%), much lower than the values obtained on simulated falls. The number of false alarms generated by the algorithms during 1-day monitoring of three representative fallers ranged from 3 to 85. The factors that affect the performance of the published algorithms, when they are applied to the real-world falls, are also discussed. These findings indicate the importance of testing fall-detection algorithms in real-life conditions in order to produce more effective automated alarm systems with higher acceptance. Further, the present results support the idea that a large, shared real-world fall database could, potentially, provide an enhanced understanding of the fall process and the information needed to design and evaluate a high-performance fall detector.Fabio BagalàClemens BeckerAngelo CappelloLorenzo ChiariKamiar AminianJeffrey M HausdorffWiebren ZijlstraJochen KlenkPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 5, p e37062 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Fabio Bagalà Clemens Becker Angelo Cappello Lorenzo Chiari Kamiar Aminian Jeffrey M Hausdorff Wiebren Zijlstra Jochen Klenk Evaluation of accelerometer-based fall detection algorithms on real-world falls.
description	Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elderly. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing some of the negative consequences of falls. Many different approaches have been explored to automatically detect a fall using inertial sensors. Although previously published algorithms report high sensitivity (SE) and high specificity (SP), they have usually been tested on simulated falls performed by healthy volunteers. We recently collected acceleration data during a number of real-world falls among a patient population with a high-fall-risk as part of the SensAction-AAL European project. The aim of the present study is to benchmark the performance of thirteen published fall-detection algorithms when they are applied to the database of 29 real-world falls. To the best of our knowledge, this is the first systematic comparison of fall detection algorithms tested on real-world falls. We found that the SP average of the thirteen algorithms, was (mean ± std) 83.0% ± 30.3% (maximum value = 98%). The SE was considerably lower (SE = 57.0% ± 27.3%, maximum value = 82.8%), much lower than the values obtained on simulated falls. The number of false alarms generated by the algorithms during 1-day monitoring of three representative fallers ranged from 3 to 85. The factors that affect the performance of the published algorithms, when they are applied to the real-world falls, are also discussed. These findings indicate the importance of testing fall-detection algorithms in real-life conditions in order to produce more effective automated alarm systems with higher acceptance. Further, the present results support the idea that a large, shared real-world fall database could, potentially, provide an enhanced understanding of the fall process and the information needed to design and evaluate a high-performance fall detector.
format	article
author	Fabio Bagalà Clemens Becker Angelo Cappello Lorenzo Chiari Kamiar Aminian Jeffrey M Hausdorff Wiebren Zijlstra Jochen Klenk
author_facet	Fabio Bagalà Clemens Becker Angelo Cappello Lorenzo Chiari Kamiar Aminian Jeffrey M Hausdorff Wiebren Zijlstra Jochen Klenk
author_sort	Fabio Bagalà
title	Evaluation of accelerometer-based fall detection algorithms on real-world falls.
title_short	Evaluation of accelerometer-based fall detection algorithms on real-world falls.
title_full	Evaluation of accelerometer-based fall detection algorithms on real-world falls.
title_fullStr	Evaluation of accelerometer-based fall detection algorithms on real-world falls.
title_full_unstemmed	Evaluation of accelerometer-based fall detection algorithms on real-world falls.
title_sort	evaluation of accelerometer-based fall detection algorithms on real-world falls.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/b5945b4b3dc04fe591c32b8a96f30168
work_keys_str_mv	AT fabiobagala evaluationofaccelerometerbasedfalldetectionalgorithmsonrealworldfalls AT clemensbecker evaluationofaccelerometerbasedfalldetectionalgorithmsonrealworldfalls AT angelocappello evaluationofaccelerometerbasedfalldetectionalgorithmsonrealworldfalls AT lorenzochiari evaluationofaccelerometerbasedfalldetectionalgorithmsonrealworldfalls AT kamiaraminian evaluationofaccelerometerbasedfalldetectionalgorithmsonrealworldfalls AT jeffreymhausdorff evaluationofaccelerometerbasedfalldetectionalgorithmsonrealworldfalls AT wiebrenzijlstra evaluationofaccelerometerbasedfalldetectionalgorithmsonrealworldfalls AT jochenklenk evaluationofaccelerometerbasedfalldetectionalgorithmsonrealworldfalls
_version_	1718423688675590144

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

Ejemplares similares