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A neural network based fall detector

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Summary:In this project we present an intelligent fall detector system based on a 3-axis accelerometer and a neural network model that allows recognizing several possible motion situations and performing an emergency call only when a fall situation occurs, with low false negatives rate and low false positives rate. The system is based on a two module platform. The first one is a Mobile Station (MS) and should be carried always by the person. An accelerometer is implemented in this module and its information is transferred via a radio-frequency channel (RF) to the Base Station (BS). The BS is fixed and is connected to a GSM (Global System for Mobile communication) module. A neural network model was built into the BS and is able to identify falls from other possible motion situations, based on the received information. According to the neural network response the system sends a SMS (Short Message Service) to a destination number requesting for assistance.
Main Authors:Rodrigues, Pedro João
Other Authors:Amaral, J.S.; Igrejas, Getúlio
Subject:Fall detector Neural network
Year:2010
Country:Portugal
Document type:conference paper
Access type:open access
Associated institution:Instituto Politécnico de Bragança
Language:English
Origin:Biblioteca Digital do IPB
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author Rodrigues, Pedro João
author2 Amaral, J.S.
Igrejas, Getúlio
author2_role author
author
author_facet Rodrigues, Pedro João
Amaral, J.S.
Igrejas, Getúlio
author_role author
contributor_name_str_mv Biblioteca Digital do IPB
country_str PT
creators_json_txt [{\"Person.name\":\"Rodrigues, Pedro João\",\"Person.identifier.orcid\":\"0000-0002-0555-2029\"},{\"Person.name\":\"Amaral, J.S.\",\"Person.identifier.orcid\":\"0000-0002-3648-7303\"},{\"Person.name\":\"Igrejas, Getúlio\",\"Person.identifier.orcid\":\"0000-0002-6820-8858\"}]
datacite.contributors.contributor.contributorName.fl_str_mv Biblioteca Digital do IPB
datacite.creators.creator.creatorName.fl_str_mv Rodrigues, Pedro João
Amaral, J.S.
Igrejas, Getúlio
datacite.date.Accepted.fl_str_mv 2010-01-01T00:00:00Z
datacite.date.available.fl_str_mv 2011-06-01T09:31:07Z
datacite.date.embargoed.fl_str_mv 2011-06-01T09:31:07Z
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_abf2
datacite.subjects.subject.fl_str_mv Fall detector
Neural network
datacite.titles.title.fl_str_mv A neural network based fall detector
dc.contributor.none.fl_str_mv Biblioteca Digital do IPB
dc.creator.none.fl_str_mv Rodrigues, Pedro João
Amaral, J.S.
Igrejas, Getúlio
dc.date.Accepted.fl_str_mv 2010-01-01T00:00:00Z
dc.date.available.fl_str_mv 2011-06-01T09:31:07Z
dc.date.embargoed.fl_str_mv 2011-06-01T09:31:07Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv http://hdl.handle.net/10198/4829
dc.language.none.fl_str_mv eng
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.subject.none.fl_str_mv Fall detector
Neural network
dc.title.fl_str_mv A neural network based fall detector
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_5794
description In this project we present an intelligent fall detector system based on a 3-axis accelerometer and a neural network model that allows recognizing several possible motion situations and performing an emergency call only when a fall situation occurs, with low false negatives rate and low false positives rate. The system is based on a two module platform. The first one is a Mobile Station (MS) and should be carried always by the person. An accelerometer is implemented in this module and its information is transferred via a radio-frequency channel (RF) to the Base Station (BS). The BS is fixed and is connected to a GSM (Global System for Mobile communication) module. A neural network model was built into the BS and is able to identify falls from other possible motion situations, based on the received information. According to the neural network response the system sends a SMS (Short Message Service) to a destination number requesting for assistance.
dirty 0
eu_rights_str_mv openAccess
format conferencePaper
fulltext.url.fl_str_mv https://bibliotecadigital.ipb.pt/bitstreams/9eb0cbd9-03e6-4971-adbe-4db1a072cb47/download
id ipb_01a049d2beb7df93b0d9ba4470f30dca
identifier.url.fl_str_mv http://hdl.handle.net/10198/4829
instacron_str ipb
institution Instituto Politécnico de Bragança
instname_str Instituto Politécnico de Bragança
language eng
network_acronym_str ipb
network_name_str Biblioteca Digital do IPB
oai_identifier_str oai:bibliotecadigital.ipb.pt:10198/4829
organization_str_mv urn:organizationAcronym:ipb
person_str_mv Rodrigues, Pedro João
Rodrigues, Pedro João
https://www.ciencia-id.pt/1316-21BB-9015
1316-21BB-9015
http://orcid.org/0000-0002-0555-2029
0000-0002-0555-2029
Amaral, J.S.
Amaral, J.S.
https://www.ciencia-id.pt/5319-7DE8-BEDA
5319-7DE8-BEDA
http://orcid.org/0000-0002-3648-7303
0000-0002-3648-7303
Igrejas, Getúlio
Igrejas, Getúlio
http://orcid.org/0000-0002-6820-8858
0000-0002-6820-8858
publishDate 2010
reponame_str Biblioteca Digital do IPB
repository_id_str urn:repositoryAcronym:ipb
service_str_mv urn:repositoryAcronym:ipb
spelling engporIn this project we present an intelligent fall detector system based on a 3-axis accelerometer and a neural network model that allows recognizing several possible motion situations and performing an emergency call only when a fall situation occurs, with low false negatives rate and low false positives rate. The system is based on a two module platform. The first one is a Mobile Station (MS) and should be carried always by the person. An accelerometer is implemented in this module and its information is transferred via a radio-frequency channel (RF) to the Base Station (BS). The BS is fixed and is connected to a GSM (Global System for Mobile communication) module. A neural network model was built into the BS and is able to identify falls from other possible motion situations, based on the received information. According to the neural network response the system sends a SMS (Short Message Service) to a destination number requesting for assistance.application/pdfporA neural network based fall detectorPersonalRodrigues, Pedro JoãoDSpacehttp://dspace.org/items/6c5911a6-b62b-4876-9def-60096b52383aDSpacehttp://dspace.org/items/6c5911a6-b62b-4876-9def-60096b52383aRodriguesPedro JoãoCiência IDhttps://www.ciencia-id.pt1316-21BB-9015ORCIDhttp://orcid.org0000-0002-0555-2029PersonalAmaral, J.S.DSpacehttp://dspace.org/items/42be2cf4-adc4-4e7f-ac60-7aab515b38cdDSpacehttp://dspace.org/items/42be2cf4-adc4-4e7f-ac60-7aab515b38cdAmaralJoana S.Ciência IDhttps://www.ciencia-id.pt5319-7DE8-BEDAORCIDhttp://orcid.org0000-0002-3648-7303PersonalIgrejas, GetúlioDSpacehttp://dspace.org/items/ab4092ec-d1b1-4fe0-b65a-efba1310fd5aDSpacehttp://dspace.org/items/ab4092ec-d1b1-4fe0-b65a-efba1310fd5aIgrejasGetúlioORCIDhttp://orcid.org0000-0002-6820-8858Researcher IDhttps://www.researcherid.comM-8571-2013Scopus Author IDhttps://www.scopus.com47761255900HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISBNIsPartOf978-972-669-990-32011-06-01T09:31:07Z20102010-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/4829http://purl.org/coar/access_right/c_abf2open accessFall detectorNeural network385119 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paperhttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/9eb0cbd9-03e6-4971-adbe-4db1a072cb47/downloadRECPAD 2010Vila Real
spellingShingle A neural network based fall detector
Rodrigues, Pedro João
Fall detector
Neural network
status SINGLETON
subject.fl_str_mv Fall detector
Neural network
title A neural network based fall detector
title_full A neural network based fall detector
title_fullStr A neural network based fall detector
title_full_unstemmed A neural network based fall detector
title_short A neural network based fall detector
title_sort A neural network based fall detector
topic Fall detector
Neural network
topic_facet Fall detector
Neural network
url http://hdl.handle.net/10198/4829
visible 1

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