Deep Learning Human Activity Recognition

Show simple item record Browne, David Giering, Michael Prestwich, Steven D.
dc.contributor.editor Curry, Edward
dc.contributor.editor Keane, Mark
dc.contributor.editor Ojo, Adegboyega
dc.contributor.editor Salwala, Dhaval 2020-05-18T11:58:39Z 2020-05-18T11:58:39Z 2019-12
dc.identifier.citation Browne, D., Giering, M. and Prestwich, S. (2019) 'Deep Learning Human Activity Recognition', Proceedings of the 27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2019), NUI Galway, Ireland, 5-6 December. CEUR Workshop Proceedings, Vol. 2563, pp. 76-87. Available at: (Accessed: 18 May 2020) en
dc.identifier.volume 2563 en
dc.identifier.startpage 76 en
dc.identifier.endpage 87 en
dc.identifier.issn 1613-0073
dc.description.abstract Human activity recognition is an area of interest in various domains such as elderly and health care, smart-buildings and surveillance, with multiple approaches to solving the problem accurately and efficiently. For many years hand-crafted features were manually extracted from raw data signals, and activities were classified using support vector machines and hidden Markov models. To further improve on this method and to extract relevant features in an automated fashion, deep learning methods have been used. The most common of these methods are Long Short-Term Memory models (LSTM), which can take the sequential nature of the data into consideration and outperform existing techniques, but which have two main pitfalls; longer training times and loss of distant pass memory. A relevantly new type of network, the Temporal Convolutional Network (TCN), overcomes these pitfalls, as it takes significantly less time to train than LSTMs and also has a greater ability to capture more of the long term dependencies than LSTMs. When paired with a Convolutional Auto-Encoder (CAE) to remove noise and reduce the complexity of the problem, our results show that both models perform equally well, achieving state-of-the-art results, but when tested for robustness on temporal data the TCN outperforms the LSTM. The results also show, for industry applications, the TCN can accurately be used for fall detection or similar events within a smart building environment. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Sun SITE Central Europe en
dc.rights © 2019, the Authors. This paper is published under the Creative Commons License Attribution 4.0 International (CC BY 4.0). en
dc.rights.uri en
dc.subject Temporal Convolutional Network en
dc.subject TCN en
dc.subject Convolutional Auto-Encoder en
dc.subject CAE en
dc.subject Fall detection en
dc.subject Smart building en
dc.subject Long Short-Term Memory en
dc.subject LSTM en
dc.title Deep Learning Human Activity Recognition en
dc.type Conference item en
dc.internal.authorcontactother Steven David Prestwich, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: en
dc.internal.availability Full text available en 2020-05-18T11:34:21Z
dc.description.version Published Version en
dc.internal.rssid 514881513
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder United Technologies Research Center en
dc.description.status Peer reviewed en
dc.identifier.journaltitle CEUR Workshop Proceedings en
dc.internal.copyrightchecked Yes
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation NUI Galway, Ireland en
dc.internal.IRISemailaddress en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ en

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© 2019, the Authors. This paper is published under the Creative Commons License Attribution 4.0 International (CC BY 4.0). Except where otherwise noted, this item's license is described as © 2019, the Authors. This paper is published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).
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