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dc.contributor.authorMalshika Welhenge, Anuradhi
dc.contributor.authorTaparugssanagorn, A.
dc.date.accessioned2023-02-02T07:05:10Z
dc.date.available2023-02-02T07:05:10Z
dc.date.issued2019
dc.identifier.citationMalshika Welhenge, A. and Taparugssanagorn, A. 2019. Human activity classification using long short-term memory network. Signal, Image and Video Processing. 13 (4): pp. 651-656.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/90320
dc.identifier.doi10.1007/s11760-018-1393-7
dc.description.abstract

Activities of daily living (ADL) can be used to identify a person’s daily routine which helps health professionals to provide preventive healthcare. Classification of ADLs is therefore very important. In this study, long short-term memory (LSTM) network, which is an extension of recurrent neural networks, is used. Data collected in MobiAct data set are used to train and test the network. An accuracy of 0.90 is achieved using LSTM network.

dc.languageEnglish
dc.publisherSPRINGER LONDON LTD
dc.subjectScience & Technology
dc.subjectTechnology
dc.subjectEngineering, Electrical & Electronic
dc.subjectImaging Science & Photographic Technology
dc.subjectEngineering
dc.subjectDeep learning
dc.subjectADL
dc.subjectLSTM
dc.subjectRNN
dc.subjectTRIAXIAL ACCELEROMETER
dc.subjectFALL DETECTION
dc.titleHuman activity classification using long short-term memory network
dc.typeJournal Article
dcterms.source.volume13
dcterms.source.number4
dcterms.source.startPage651
dcterms.source.endPage656
dcterms.source.issn1863-1703
dcterms.source.titleSignal, Image and Video Processing
dc.date.updated2023-02-02T07:05:10Z
curtin.accessStatusFulltext not available
curtin.contributor.orcidMalshika Welhenge, Anuradhi [0000-0001-9219-2246]
dcterms.source.eissn1863-1711
curtin.contributor.scopusauthoridMalshika Welhenge, Anuradhi [56604130200]


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