Human Falling Recognition Based on Movement Energy Expenditure Feature

Falls in the elderly are a common phenomenon in daily life, which causes serious injuries and even death. Human activity recognition methods with wearable sensor signals as input have been proposed to improve the accuracy and automation of daily falling recognition. In order not to affect the normal...

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Autores principales:	Daohua Pan, Hongwei Liu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/fb1c59dd4e7c4000b2f8be322c91b171
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id	oai:doaj.org-article:fb1c59dd4e7c4000b2f8be322c91b171
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spelling	oai:doaj.org-article:fb1c59dd4e7c4000b2f8be322c91b1712021-11-22T01:09:50ZHuman Falling Recognition Based on Movement Energy Expenditure Feature1607-887X10.1155/2021/1422586https://doaj.org/article/fb1c59dd4e7c4000b2f8be322c91b1712021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1422586https://doaj.org/toc/1607-887XFalls in the elderly are a common phenomenon in daily life, which causes serious injuries and even death. Human activity recognition methods with wearable sensor signals as input have been proposed to improve the accuracy and automation of daily falling recognition. In order not to affect the normal life behavior of the elderly, to make full use of the functions provided by the smartphone, to reduce the inconvenience caused by wearing sensor devices, and to reduce the cost of monitoring systems, the accelerometer and gyroscope integrated inside the smartphone are employed to collect the behavioral data of the elderly in their daily lives, and the threshold analysis method is used to study the human falling behavior recognition. Based on this, a three-level threshold detection algorithm for human fall behavior recognition is proposed by introducing human movement energy expenditure as a new feature. The algorithm integrates the changes of human movement energy expenditure, combined acceleration, and body tilt angle in the process of falling, which alleviates the problem of misjudgment caused by using only the threshold information of acceleration or (and) angle change to discriminate falls and improves the recognition accuracy. The recognition accuracy of this algorithm is verified by experiments to reach 95.42%. The APP is also devised to realize the timely detection of fall behavior and send alarms automatically.Daohua PanHongwei LiuHindawi LimitedarticleMathematicsQA1-939ENDiscrete Dynamics in Nature and Society, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mathematics QA1-939
spellingShingle	Mathematics QA1-939 Daohua Pan Hongwei Liu Human Falling Recognition Based on Movement Energy Expenditure Feature
description	Falls in the elderly are a common phenomenon in daily life, which causes serious injuries and even death. Human activity recognition methods with wearable sensor signals as input have been proposed to improve the accuracy and automation of daily falling recognition. In order not to affect the normal life behavior of the elderly, to make full use of the functions provided by the smartphone, to reduce the inconvenience caused by wearing sensor devices, and to reduce the cost of monitoring systems, the accelerometer and gyroscope integrated inside the smartphone are employed to collect the behavioral data of the elderly in their daily lives, and the threshold analysis method is used to study the human falling behavior recognition. Based on this, a three-level threshold detection algorithm for human fall behavior recognition is proposed by introducing human movement energy expenditure as a new feature. The algorithm integrates the changes of human movement energy expenditure, combined acceleration, and body tilt angle in the process of falling, which alleviates the problem of misjudgment caused by using only the threshold information of acceleration or (and) angle change to discriminate falls and improves the recognition accuracy. The recognition accuracy of this algorithm is verified by experiments to reach 95.42%. The APP is also devised to realize the timely detection of fall behavior and send alarms automatically.
format	article
author	Daohua Pan Hongwei Liu
author_facet	Daohua Pan Hongwei Liu
author_sort	Daohua Pan
title	Human Falling Recognition Based on Movement Energy Expenditure Feature
title_short	Human Falling Recognition Based on Movement Energy Expenditure Feature
title_full	Human Falling Recognition Based on Movement Energy Expenditure Feature
title_fullStr	Human Falling Recognition Based on Movement Energy Expenditure Feature
title_full_unstemmed	Human Falling Recognition Based on Movement Energy Expenditure Feature
title_sort	human falling recognition based on movement energy expenditure feature
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/fb1c59dd4e7c4000b2f8be322c91b171
work_keys_str_mv	AT daohuapan humanfallingrecognitionbasedonmovementenergyexpenditurefeature AT hongweiliu humanfallingrecognitionbasedonmovementenergyexpenditurefeature
_version_	1718418438154616832

Human Falling Recognition Based on Movement Energy Expenditure Feature

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