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https://hdl.handle.net/1822/68687
Registo completo
Campo DC | Valor | Idioma |
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dc.contributor.author | Ribeiro, Nuno Ferrete | por |
dc.contributor.author | André, João | por |
dc.contributor.author | Costa, Lino | por |
dc.contributor.author | Santos, Cristina | por |
dc.date.accessioned | 2020-12-22T13:13:01Z | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 0148-5598 | - |
dc.identifier.issn | 30949770 | - |
dc.identifier.uri | https://hdl.handle.net/1822/68687 | - |
dc.description.abstract | Falls are a prevalent problem in actual society. Some falls result in injuries and the cost associated with their treatment is high. This is a complex problem that requires several steps in order to be tackled. Firstly, it is crucial to develop strategies that recognize the locomotion mode, indicating the state of the subject in various situations. This article aims to develop a strategy capable of identifying normal gait, the pre-fall condition, and the fall situation, based on a wearable system (IMUs-based). This system was used to collect data from healthy subjects that mimicked falls. The strategy consists, essentially, in the construction and use of classifiers as tools for recognizing the locomotion modes. Two approaches were explored. Associative Skill Memories (ASMs) based classifier and a Convolutional Neural Network (CNN) classifier based on deep learning. Finally, these classifiers were compared, providing for a tool with a good accuracy in recognizing the locomotion modes. Results have shown that the accuracy of the classifiers was quite acceptable. The CNN presented the best results with 92.71% of accuracy considering the pre-fall step different from normal steps, and 100% when not considering. | por |
dc.description.sponsorship | This work has been supported by the FCT - Fundação para a Ciencia e Tecnologia - with the scholarship reference PD/BD/141515/2018, by the FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) and P2020 with the Reference Project EML under Grant POCI-01-0247-FEDER-033067, and through the COMPETE 2020 POCI with the Reference Project under Grant POCI-01-0145-FEDER006941. | por |
dc.language.iso | eng | por |
dc.publisher | Springer | por |
dc.relation | PD/BD/141515/2018 | por |
dc.rights | restrictedAccess | por |
dc.subject | Associative Skill Memories (ASMs) | por |
dc.subject | Convolutional Neural Network (CNN) | por |
dc.subject | Deep learning | por |
dc.subject | Gait analysis | por |
dc.subject | Inertial Measurement Units (IMUs) | por |
dc.subject | Principal Component Analysis (PCA) | por |
dc.title | Development of a strategy to predict and detect falls using wearable sensors | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://link.springer.com/article/10.1007/s10916-019-1252-2 | por |
oaire.citationIssue | 5 | por |
oaire.citationVolume | 43 | por |
dc.date.updated | 2020-12-22T10:47:04Z | - |
dc.identifier.doi | 10.1007/s10916-019-1252-2 | por |
dc.date.embargo | 10000-01-01 | - |
dc.identifier.pmid | 30949770 | por |
dc.subject.wos | Science & Technology | - |
sdum.export.identifier | 7632 | - |
sdum.journal | Journal of Medical Systems | por |
dc.identifier.pmc | 30949770 | - |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals CMEMS - Artigos em revistas internacionais/Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Ribeiro2019_Article_DevelopmentOfAStrategyToPredic.pdf Acesso restrito! | 1,61 MB | Adobe PDF | Ver/Abrir |