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dc.contributor.authorYao, Lina
dc.contributor.authorSheng, Quan Z
dc.contributor.authorLi, Xue
dc.contributor.authorGu, Tao
dc.contributor.authorTan, Mingkui
dc.contributor.authorWang, Xianzhi
dc.contributor.authorWang, Sen
dc.contributor.authorRuan, Wenjie
dc.date.accessioned2019-07-04T12:39:20Z
dc.date.available2019-07-04T12:39:20Z
dc.date.issued2018
dc.identifier.issn1536-1233
dc.identifier.doi10.1109/TMC.2017.2706282
dc.identifier.urihttp://hdl.handle.net/10072/380433
dc.description.abstractUnderstanding and recognizing human activities is a fundamental research topic for a wide range of important applications such as fall detection and remote health monitoring and intervention. Despite active research in human activity recognition over the past years, existing approaches based on computer vision or wearable sensor technologies present several significant issues such as privacy (e.g., using video camera to monitor the elderly at home) and practicality (e.g., not possible for an older person with dementia to remember wearing devices). In this paper, we present a low-cost, unobtrusive, and robust system that supports independent living of older people. The system interprets what a person is doing by deciphering signal fluctuations using radio-frequency identification (RFID) technology and machine learning algorithms. To deal with noisy, streaming, and unstable RFID signals, we develop a compressive sensing, dictionary-based approach that can learn a set of compact and informative dictionaries of activities using an unsupervised subspace decomposition. In particular, we devise a number of approaches to explore the properties of sparse coefficients of the learned dictionaries for fully utilizing the embodied discriminative information on the activity recognition task. Our approach achieves efficient and robust activity recognition via a more compact and robust representation of activities. Extensive experiments conducted in a real-life residential environment demonstrate that our proposed system offers a good overall performance and shows the promising practical potential to underpin the applications for the independent living of the elderly.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers
dc.publisher.placeUnited States
dc.relation.ispartofpagefrom293
dc.relation.ispartofpageto306
dc.relation.ispartofissue2
dc.relation.ispartofjournalIEEE Transactions on Mobile Computing
dc.relation.ispartofvolume17
dc.subject.fieldofresearchElectrical and Electronic Engineering not elsewhere classified
dc.subject.fieldofresearchDistributed Computing
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchCommunications Technologies
dc.subject.fieldofresearchcode090699
dc.subject.fieldofresearchcode0805
dc.subject.fieldofresearchcode0906
dc.subject.fieldofresearchcode1005
dc.titleCompressive Representation for Device-Free Activity Recognition with Passive RFID Signal Strength
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorWang, Sen


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