Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/128389
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dc.contributor.authorHussain, Z.en
dc.contributor.authorSheng, Q.en
dc.contributor.authorZhang, W.en
dc.date.issued2020en
dc.identifier.citationJournal of Network and Computer Applications, 2020; 167:1-22en
dc.identifier.issn1084-8045en
dc.identifier.issn1095-8592en
dc.identifier.urihttp://hdl.handle.net/2440/128389-
dc.description.abstractHuman activity recognition has gained importance in recent years due to its applications in various fields such as health, security and surveillance, entertainment, and intelligent environments. A significant amount of work has been done on human activity recognition and researchers have leveraged different approaches, such as wearable, object-tagged, and device-free, to recognize human activities. In this article, we present a comprehensive survey of the work conducted over the 10-year period of 2010–2019 in various areas of human activity recognition with main focus on device-free solutions. The device-free approach is becoming very popular due to the fact that the subject is not required to carry anything. Instead, the environment is tagged with devices to capture the required information. We propose a new taxonomy for categorizing the research work conducted in the field of activity recognition and divide the existing literature into three sub-areas: action-based, motion-based, and interaction-based. We further divide these areas into ten different sub-topics and present the latest research works in these sub-topics. Unlike previous surveys which focus only on one type of activities, to the best of our knowledge, we cover all the sub-areas in activity recognition and provide a comparison of the latest research work in these sub-areas. Specifically, we discuss the key attributes and design approaches for the work presented. Then we provide extensive analysis based on 10 important metrics, to present a comprehensive overview of the state-of-the-art techniques and trends in different sub-areas of device-free human activity recognition. In the end, we discuss open research issues and propose future research directions in the field of human activity recognition.en
dc.description.statementofresponsibilityZawar Hussain, Quan Z.Sheng, Wei Emma Zhangen
dc.language.isoenen
dc.publisherElsevieren
dc.rights© 2020 Elsevier Ltd. All rights reserved.en
dc.subjectHuman activity recognition; gesture recognition; motion detection; device-free; dense sensing; human object interaction; RFID; internet of thingsen
dc.titleA review and categorization of techniques on device-free human activity recognitionen
dc.typeJournal articleen
dc.identifier.rmid1000023058en
dc.identifier.doi10.1016/j.jnca.2020.102738en
dc.relation.granthttp://purl.org/au-research/grants/arc/DP130104614en
dc.relation.granthttp://purl.org/au-research/grants/arc/LP190100140en
dc.relation.granthttp://purl.org/au-research/grants/arc/FT140101247en
dc.relation.granthttp://purl.org/au-research/grants/arc/LE180100158en
dc.identifier.pubid537845-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS10en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidZhang, W. [0000-0002-0406-5974]en
Appears in Collections:Computer Science publications

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