An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box

Abstract Falls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in time. Current detection methods have one or more of the following problems: frequent battery replacements, wearer discomfort, high costs, com...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Francy Shu, Jeff Shu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7a25bc620b38442688d87bded839c2eb
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7a25bc620b38442688d87bded839c2eb
record_format	dspace
spelling	oai:doaj.org-article:7a25bc620b38442688d87bded839c2eb2021-12-02T14:16:17ZAn eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box10.1038/s41598-021-81115-92045-2322https://doaj.org/article/7a25bc620b38442688d87bded839c2eb2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81115-9https://doaj.org/toc/2045-2322Abstract Falls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in time. Current detection methods have one or more of the following problems: frequent battery replacements, wearer discomfort, high costs, complicated setup, furniture occlusion, and intensive computation. In fact, all non-wearable methods fail to detect falls beyond ten meters. Here, we design a house-wide fall detection system capable of detecting stumbling, slipping, fainting, and various other types of falls at 60 m and beyond, including through transparent glasses, screens, and rain. By analyzing the fall pattern using machine learning and crafted rules via a local, low-cost single-board computer, true falls can be differentiated from daily activities and monitored through conventionally available surveillance systems. Either a multi-camera setup in one room or single cameras installed at high altitudes can avoid occlusion. This system’s flexibility enables a wide-coverage set-up, ensuring safety in senior homes, rehab centers, and nursing facilities. It can also be configured into high-precision and high-recall application to capture every single fall in high-risk zones.Francy ShuJeff ShuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Francy Shu Jeff Shu An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box
description	Abstract Falls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in time. Current detection methods have one or more of the following problems: frequent battery replacements, wearer discomfort, high costs, complicated setup, furniture occlusion, and intensive computation. In fact, all non-wearable methods fail to detect falls beyond ten meters. Here, we design a house-wide fall detection system capable of detecting stumbling, slipping, fainting, and various other types of falls at 60 m and beyond, including through transparent glasses, screens, and rain. By analyzing the fall pattern using machine learning and crafted rules via a local, low-cost single-board computer, true falls can be differentiated from daily activities and monitored through conventionally available surveillance systems. Either a multi-camera setup in one room or single cameras installed at high altitudes can avoid occlusion. This system’s flexibility enables a wide-coverage set-up, ensuring safety in senior homes, rehab centers, and nursing facilities. It can also be configured into high-precision and high-recall application to capture every single fall in high-risk zones.
format	article
author	Francy Shu Jeff Shu
author_facet	Francy Shu Jeff Shu
author_sort	Francy Shu
title	An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box
title_short	An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box
title_full	An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box
title_fullStr	An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box
title_full_unstemmed	An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box
title_sort	eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7a25bc620b38442688d87bded839c2eb
work_keys_str_mv	AT francyshu aneightcamerafalldetectionsystemusinghumanfallpatternrecognitionviamachinelearningbyalowcostandroidbox AT jeffshu aneightcamerafalldetectionsystemusinghumanfallpatternrecognitionviamachinelearningbyalowcostandroidbox AT francyshu eightcamerafalldetectionsystemusinghumanfallpatternrecognitionviamachinelearningbyalowcostandroidbox AT jeffshu eightcamerafalldetectionsystemusinghumanfallpatternrecognitionviamachinelearningbyalowcostandroidbox
_version_	1718391675865268224

An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box

Ejemplares similares