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dc.contributor.authorOkeyo, Georgeen
dc.contributor.authorChen, Limingen
dc.contributor.authorWang, H.en
dc.contributor.authorSterritt, Royen
dc.date.accessioned2017-03-21T10:36:29Z
dc.date.available2017-03-21T10:36:29Z
dc.date.issued2012-12-03
dc.identifier.citationOkeyo, G. et al. (2012) Dynamic Sensor Data Segmentation for Real time Activity Recognition. Pervasive and Mobile Computing, 10, (B), pp. 155-172en
dc.identifier.issn1574-1192
dc.identifier.urihttp://hdl.handle.net/2086/13776
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractApproaches and algorithms for activity recognition have recently made substantial progress due to advancements in pervasive and mobile computing, smart environments and ambient assisted living. Nevertheless, it is still difficult to achieve real-time continuous activity recognition as sensor data segmentation remains a challenge. This paper presents a novel approach to real-time sensor data segmentation for continuous activity recognition. Central to the approach is a dynamic segmentation model, based on the notion of varied time windows, which can shrink and expand the segmentation window size by using temporal information of sensor data and activities as well as the state of activity recognition. The paper first analyzes the characteristics of activities of daily living from which the segmentation model that is applicable to a wide range of activity recognition scenarios is motivated and developed. It then describes the working mechanism and relevant algorithms of the model in the context of knowledge-driven activity recognition based on ontologies. The presented approach has been implemented in a prototype system and evaluated in a number of experiments. Results have shown average recognition accuracy above 83% in all experiments for real time activity recognition, which proves the approach and the underlying model.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectOntologyen
dc.subjectSensor data segmentationen
dc.subjectTime windowen
dc.subjectReal-time activity recognitionen
dc.subjectOntological activity modellingen
dc.subjectTemporal informationen
dc.titleDynamic Sensor Data Segmentation for Real time Activity Recognitionen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1016/j.pmcj.2012.11.004
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2012-11-23en
dc.researchinstituteCyber Technology Institute (CTI)en


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