Human activity recognition using tag-based radio frequency localization

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dc.citation.epage179en_US
dc.citation.issueNumber2en_US
dc.citation.spage153en_US
dc.citation.volumeNumber30en_US
dc.contributor.authorYurtman, A.en_US
dc.contributor.authorBarshan, B.en_US
dc.date.accessioned2018-04-12T10:46:15Z
dc.date.available2018-04-12T10:46:15Z
dc.date.issued2016en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractThis article provides a comparative study on the different techniques of classifying human activities using tag-based radio-frequency (RF) localization. A publicly available dataset is used where the position data of multiple RF tags worn on different parts of the human body are acquired asynchronously and nonuniformly. In this study, curves fitted to the data are resampled uniformly and then segmented. We investigate the effect on system accuracy of varying the relevant system parameters. We compare various curve-fitting, segmentation, and classification techniques and present the combination resulting in the best performance. The classifiers are validated using 5-fold and subject-based leave-one-out cross validation, and for the complete classification problem with 11 classes, the proposed system demonstrates an average classification error of 8.67% and 21.30%, respectively. When the number of classes is reduced to five by omitting the transition classes, these errors become 1.12% and 6.52%, respectively. The results indicate that the system demonstrates acceptable classification performance despite that tag-based RF localization does not provide very accurate position measurements.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T10:46:15Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2016en
dc.identifier.doi10.1080/08839514.2016.1138787en_US
dc.identifier.issn0883-9514
dc.identifier.urihttp://hdl.handle.net/11693/36624
dc.language.isoEnglishen_US
dc.publisherTaylor and Francis Inc.en_US
dc.relation.isversionofhttp://dx.doi.org/10.1080/08839514.2016.1138787en_US
dc.source.titleApplied Artificial Intelligenceen_US
dc.subjectRadio wavesen_US
dc.subjectStatistical methodsen_US
dc.subjectClassification errorsen_US
dc.subjectClassification performanceen_US
dc.subjectClassification techniqueen_US
dc.subjectComparative studiesen_US
dc.subjectComplete classificationen_US
dc.subjectHuman activity recognitionen_US
dc.subjectLeave-one-out cross validationsen_US
dc.subjectRadio frequency localizationsen_US
dc.subjectCurve fittingen_US
dc.titleHuman activity recognition using tag-based radio frequency localizationen_US
dc.typeArticleen_US

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