Activity Recognition Evaluation Via Machine Learning

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aut.relation.articlenumbere3en_NZ
aut.relation.journalEAI Endorsed Transactions on Ambient Systemsen_NZ
aut.relation.volume18en_NZ
aut.researcherConnor, Andrew
dc.contributor.authorRameka, ANAen_NZ
dc.contributor.authorConnor, AMen_NZ
dc.contributor.authorKruse, Jen_NZ
dc.date.accessioned2019-11-18T03:39:08Z
dc.date.available2019-11-18T03:39:08Z
dc.date.copyright2019-11-15en_NZ
dc.date.issued2019-11-15en_NZ
dc.description.abstractWith the proliferation of relatively cheap Internet of Things (IoT) devices, smart environments have been highlighted as an example of how the IoT can make our lives easier. Each of these ‘things’ produces data which can work in unison to react to its users. Machine learning makes use of this data to make inferences about our habits and activities, such as our buying preferences or likely commute destinations. However, this level of human inclusion within the IoT relies on indirect inferences from the usage of these devices or services. Activity recognition is already a widely researched area and could provide a more direct way of including humans within this system. This research explores the feasibility of using a cost effective, unobtrusive, single modality ground-based sensor matrix to track subtle pressure changes to predict user activity, in an effort to assess its ability to act as an intermediary interface between humans and digital systems such as the IoT.en_NZ
dc.identifier.citationAMSYS 18: e3; doi: 10.4108/eai.23-3-2018.161436
dc.identifier.doi10.4108/eai.23-3-2018.161436en_NZ
dc.identifier.issn2032-927Xen_NZ
dc.identifier.urihttps://hdl.handle.net/10292/13012
dc.languageenen_NZ
dc.publisherEuropean Alliance for Innovation (EAI)en_NZ
dc.relation.urihttps://eudl.eu/doi/10.4108/eai.23-3-2018.161436en_NZ
dc.rightsCopyright © 2019 A.N.A. Rameka et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectActivity Recognition; Machine Learning; Internet of Things; Smart Floors; Smart Chairs; Smart Environments
dc.titleActivity Recognition Evaluation Via Machine Learningen_NZ
dc.typeJournal Article
pubs.elements-id366027
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
pubs.organisational-data/AUT/Design & Creative Technologies/CoLab
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF CoLab
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