Context-based healthy lifestyle recommendation for enhancing user's wellness

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dc.contributor.authorLim, Chae-Gyunko
dc.contributor.authorKim, Zae Myungko
dc.contributor.authorChoi, Ho-Jinko
dc.date.accessioned2018-01-23T01:16:10Z-
dc.date.available2018-01-23T01:16:10Z-
dc.date.created2017-12-28-
dc.date.created2017-12-28-
dc.date.created2017-12-28-
dc.date.issued2017-02-13-
dc.identifier.citationIEEE International Conference on Big Data and Smart Computing (BigComp), pp.418 - 421-
dc.identifier.issn2375-933X-
dc.identifier.urihttp://hdl.handle.net/10203/237789-
dc.description.abstractIn this paper, we propose a service to predict changes in a user's daily activity in consideration of the user's contextual information and recommend appropriate physical activities for improving well-being. Based on the data collected from a smartphone and wearable sensor, our service models an activity pattern of the specific user and predicts the upcoming activity changes of the user by using the individual's lifestyle model. In addition, we continually observe changes in daily activity to determine appropriate preferred activities for each user, and recommend optimal activities to promote wellness by tracking changes in contextual information and user feedback. It can be confirmed that the use of the proposed service contributes to the improvement of the user's wellness by performing a clinical experiment on the actual subjects.-
dc.languageEnglish-
dc.publisherIEEE-
dc.titleContext-based healthy lifestyle recommendation for enhancing user's wellness-
dc.typeConference-
dc.identifier.wosid000403390900072-
dc.identifier.scopusid2-s2.0-85017620839-
dc.type.rimsCONF-
dc.citation.beginningpage418-
dc.citation.endingpage421-
dc.citation.publicationnameIEEE International Conference on Big Data and Smart Computing (BigComp)-
dc.identifier.conferencecountryKO-
dc.identifier.conferencelocationMaison Glad Jeju-
dc.identifier.doi10.1109/BIGCOMP.2017.7881748-
dc.contributor.localauthorChoi, Ho-Jin-
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