Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/80702
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dc.contributor.authorAbu Alsheikh, Mohammaden
dc.contributor.authorSelim, Ahmeden
dc.contributor.authorNiyato, Dusiten
dc.contributor.authorDoyle, Lindaen
dc.contributor.authorLin, Shaoweien
dc.contributor.authorTan, Hwee-Pinken
dc.date.accessioned2017-03-24T08:07:35Zen
dc.date.accessioned2019-12-06T13:55:01Z-
dc.date.available2017-03-24T08:07:35Zen
dc.date.available2019-12-06T13:55:01Z-
dc.date.issued2016en
dc.identifier.citationAbu Alsheikh, M., Selim, A., Niyato, D., Doyle, L., Lin, S., & Tan, H.-P. (2016). Deep activity recognition models with triaxial accelerometers. The Workshops of the Thirtieth AAAI Conference on Artificial Intelligence, 8-13.en
dc.identifier.urihttps://hdl.handle.net/10356/80702-
dc.description.abstractDespite the widespread installation of accelerometers in almost all mobile phones and wearable devices, activity recognition using accelerometers is still immature due to the poor recognition accuracy of existing recognition methods and the scarcity of labeled training data. We consider the problem of human activity recognition using triaxial accelerometers and deep learning paradigms. This paper shows that deep activity recognition models (a) provide better recognition accuracy of human activities, (b) avoid the expensive design of handcrafted features in existing systems, and (c) utilize the massive unlabeled acceleration samples for unsupervised feature extraction. Moreover, a hybrid approach of deep learning and hidden Markov models (DL-HMM) is presented for sequential activity recognition. This hybrid approach integrates the hierarchical representations of deep activity recognition models with the stochastic modeling of temporal sequences in the hidden Markov models. We show substantial recognition improvement on real world datasets over state-of-the-art methods of human activity recognition using triaxial accelerometers.en
dc.format.extent7 p.en
dc.language.isoenen
dc.rights© 2016 Association for the Advancement of Artificial Intelligence (AAAI). This is the author created version of a work that has been peer reviewed and accepted for publication by The Workshops of the Thirtieth AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI). It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [https://arxiv.org/abs/1511.04664].en
dc.subjectLearningen
dc.subjectHuman-Computer Interactionen
dc.titleDeep Activity Recognition Models with Triaxial Accelerometersen
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.contributor.conferenceThe Workshops of the Thirtieth AAAI Conference on Artificial Intelligenceen
dc.description.versionAccepted versionen
dc.identifier.urlhttps://arxiv.org/abs/1511.04664en
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