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Multi-sensor fusion for enhanced contextual awareness of everyday activities with ubiquitous devices

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dc.contributor.author Guiry, John J.
dc.contributor.author van de Ven, Pepijn
dc.contributor.author Nelson, John
dc.date.accessioned 2014-08-18T09:16:26Z
dc.date.available 2014-08-18T09:16:26Z
dc.date.issued 2014
dc.identifier.uri http://hdl.handle.net/10344/3975
dc.description peer-reviewed en_US
dc.description.abstract In this paper, the authors investigate the role that smart devices, including smartphones and smartwatches, can play in identifying activities of daily living. A feasibility study involving N = 10 participants was carried out to evaluate the devices’ ability to differentiate between nine everyday activities. The activities examined include walking, running, cycling, standing, sitting, elevator ascents, elevator descents, stair ascents and stair descents. The authors also evaluated the ability of these devices to differentiate indoors from outdoors, with the aim of enhancing contextual awareness. Data from this study was used to train and test five well known machine learning algorithms: C4.5, CART, Naïve Bayes, Multi-Layer Perceptrons and finally Support Vector Machines. Both single and multi-sensor approaches were examined to better understand the role each sensor in the device can play in unobtrusive activity recognition. The authors found overall results to be promising, with some models correctly classifying up to 100% of all instances. en_US
dc.language.iso eng en_US
dc.publisher MDPI en_US
dc.relation.ispartofseries Sensors;14, pp. 5687-5701
dc.relation.uri 1573768
dc.subject sensor fusion en_US
dc.subject ubiquitous activity monitoring en_US
dc.subject smart devices en_US
dc.subject smartphone en_US
dc.subject smartwatch en_US
dc.subject geospatial awareness en_US
dc.subject activities of daily living en_US
dc.title Multi-sensor fusion for enhanced contextual awareness of everyday activities with ubiquitous devices en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.3390/s140305687
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 1573768


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