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dc.contributor.authorRawassizadeh, Rezaen_US
dc.contributor.authorTomitsch, Martinen_US
dc.contributor.authorNourizadeh, Manouchehren_US
dc.contributor.authorMomeni, Elahehen_US
dc.contributor.authorPeery, Aaronen_US
dc.contributor.authorUlanova, Liudmilaen_US
dc.contributor.authorPazzani, Michaelen_US
dc.coverage.spatialSwitzerlanden_US
dc.date2015-08-31
dc.date.accessioned2020-05-15T19:40:15Z
dc.date.available2020-05-15T19:40:15Z
dc.date.issued2015-09-08
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/26370997
dc.identifier.citationReza Rawassizadeh, Martin Tomitsch, Manouchehr Nourizadeh, Elaheh Momeni, Aaron Peery, Liudmila Ulanova, Michael Pazzani. 2015. "Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches.." Sensors (Basel), Volume 15, Issue 9, pp. 22616 - 22645. https://doi.org/10.3390/s150922616
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/2144/40926
dc.description.abstractAs the availability and use of wearables increases, they are becoming a promising platform for context sensing and context analysis. Smartwatches are a particularly interesting platform for this purpose, as they offer salient advantages, such as their proximity to the human body. However, they also have limitations associated with their small form factor, such as processing power and battery life, which makes it difficult to simply transfer smartphone-based context sensing and prediction models to smartwatches. In this paper, we introduce an energy-efficient, generic, integrated framework for continuous context sensing and prediction on smartwatches. Our work extends previous approaches for context sensing and prediction on wrist-mounted wearables that perform predictive analytics outside the device. We offer a generic sensing module and a novel energy-efficient, on-device prediction module that is based on a semantic abstraction approach to convert sensor data into meaningful information objects, similar to human perception of a behavior. Through six evaluations, we analyze the energy efficiency of our framework modules, identify the optimal file structure for data access and demonstrate an increase in accuracy of prediction through our semantic abstraction method. The proposed framework is hardware independent and can serve as a reference model for implementing context sensing and prediction on small wearable devices beyond smartwatches, such as body-mounted cameras.en_US
dc.format.extentp. 22616 - 22645en_US
dc.languageeng
dc.language.isoen_US
dc.relation.ispartofSensors (Basel)
dc.rights"© 2015 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license."en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectEnergy efficiencyen_US
dc.subjectLifeloggingen_US
dc.subjectMobile sensingen_US
dc.subjectPredictionen_US
dc.subjectQuantified selfen_US
dc.subjectSmartwatchen_US
dc.subjectWearableen_US
dc.subjectAnalytical chemistryen_US
dc.subjectElectrical and electronic engineeringen_US
dc.subjectEnvironmental science and managementen_US
dc.subjectEcologyen_US
dc.titleEnergy-efficient integration of continuous context sensing and prediction into smartwatchesen_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.3390/s150922616
pubs.elements-sourcepubmeden_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, Metropolitan Collegeen_US
pubs.publication-statusPublished onlineen_US
dc.identifier.mycv479121


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"© 2015  by  the  authors. This article is an open access article distributed under the terms and conditions of the   Creative Commons Attribution license."
Except where otherwise noted, this item's license is described as "© 2015 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license."