Recognizing High-Level Contexts from Smartphone Built-In Sensors for Mobile Media Content Recommendation
Context Recognition is an important element for developing context aware mobile applications. However, context is mostly available as low-level sensor data that are in form not suitable for mobile applications. In this paper, we present a process that uses classifiers for recognizing high-level cont...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 147 |
|---|---|
| container_issue | |
| container_start_page | 142 |
| container_title | |
| container_volume | 2 |
| creator | Otebolaku, Abayomi M. Andrade, Maria T. |
| description | Context Recognition is an important element for developing context aware mobile applications. However, context is mostly available as low-level sensor data that are in form not suitable for mobile applications. In this paper, we present a process that uses classifiers for recognizing high-level contexts from low-level sensor data. The process demonstrates accurate recognition of user activity contexts, using smart-phone built-in sensors. We describe and illustrate our context recognition model and then demonstrate its application in a context aware mobile multimedia recommendation system. |
| doi_str_mv | 10.1109/MDM.2013.84 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6569080</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6569080</ieee_id><sourcerecordid>6569080</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-e0be6a3e3905e826027ad50cf2669f00929f0d9886d8977c77d7abcd1505e2bb3</originalsourceid><addsrcrecordid>eNotjstOwzAURM1LopSuWLLxD6Rc2_FrCS3QSo2QKEjsqjxuWqPErhKDgK8nqGxmNmeOhpArBlPGwN5k82zKgYmpSY_IBWhlZWq1eDsmIy60TEDw9IRMrDYsVVooUEaekhGTkiWKp_KcTPr-HQAYCMmEGpHdM5Zh692P81u6cNtdssJPbOgs-Ihfsad1F1q6bvMu7nfBI737cE1Mlp6u0fehG4DQ0SwUrkGaYeXyw9RH-mduW_RVHl3wl-SszpseJ_89Jq8P9y-zRbJ6elzObleJY1rGBKFAlQsUFiQaroDrvJJQ1lwpWwNYPmRljVGVsVqXWlc6L8qKyYHnRSHG5PrgdYi42XduuP69UVJZMCB-AbPXW2s</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Recognizing High-Level Contexts from Smartphone Built-In Sensors for Mobile Media Content Recommendation</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Otebolaku, Abayomi M. ; Andrade, Maria T.</creator><creatorcontrib>Otebolaku, Abayomi M. ; Andrade, Maria T.</creatorcontrib><description>Context Recognition is an important element for developing context aware mobile applications. However, context is mostly available as low-level sensor data that are in form not suitable for mobile applications. In this paper, we present a process that uses classifiers for recognizing high-level contexts from low-level sensor data. The process demonstrates accurate recognition of user activity contexts, using smart-phone built-in sensors. We describe and illustrate our context recognition model and then demonstrate its application in a context aware mobile multimedia recommendation system.</description><identifier>ISSN: 1551-6245</identifier><identifier>ISBN: 9781467360685</identifier><identifier>ISBN: 1467360686</identifier><identifier>EISSN: 2375-0324</identifier><identifier>EISBN: 076954973X</identifier><identifier>EISBN: 9780769549736</identifier><identifier>DOI: 10.1109/MDM.2013.84</identifier><identifier>CODEN: IEEPAD</identifier><language>eng</language><publisher>IEEE</publisher><subject>Accelerometers ; classification ; Context ; Context modeling ; context recognition ; low-level context data ; Mobile communication ; multimedia ; Multimedia communication ; Sensors ; smartphone sensing ; Support vector machine classification</subject><ispartof>2013 IEEE 14th International Conference on Mobile Data Management, 2013, Vol.2, p.142-147</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6569080$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6569080$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Otebolaku, Abayomi M.</creatorcontrib><creatorcontrib>Andrade, Maria T.</creatorcontrib><title>Recognizing High-Level Contexts from Smartphone Built-In Sensors for Mobile Media Content Recommendation</title><title>2013 IEEE 14th International Conference on Mobile Data Management</title><addtitle>mdm</addtitle><description>Context Recognition is an important element for developing context aware mobile applications. However, context is mostly available as low-level sensor data that are in form not suitable for mobile applications. In this paper, we present a process that uses classifiers for recognizing high-level contexts from low-level sensor data. The process demonstrates accurate recognition of user activity contexts, using smart-phone built-in sensors. We describe and illustrate our context recognition model and then demonstrate its application in a context aware mobile multimedia recommendation system.</description><subject>Accelerometers</subject><subject>classification</subject><subject>Context</subject><subject>Context modeling</subject><subject>context recognition</subject><subject>low-level context data</subject><subject>Mobile communication</subject><subject>multimedia</subject><subject>Multimedia communication</subject><subject>Sensors</subject><subject>smartphone sensing</subject><subject>Support vector machine classification</subject><issn>1551-6245</issn><issn>2375-0324</issn><isbn>9781467360685</isbn><isbn>1467360686</isbn><isbn>076954973X</isbn><isbn>9780769549736</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjstOwzAURM1LopSuWLLxD6Rc2_FrCS3QSo2QKEjsqjxuWqPErhKDgK8nqGxmNmeOhpArBlPGwN5k82zKgYmpSY_IBWhlZWq1eDsmIy60TEDw9IRMrDYsVVooUEaekhGTkiWKp_KcTPr-HQAYCMmEGpHdM5Zh692P81u6cNtdssJPbOgs-Ihfsad1F1q6bvMu7nfBI737cE1Mlp6u0fehG4DQ0SwUrkGaYeXyw9RH-mduW_RVHl3wl-SszpseJ_89Jq8P9y-zRbJ6elzObleJY1rGBKFAlQsUFiQaroDrvJJQ1lwpWwNYPmRljVGVsVqXWlc6L8qKyYHnRSHG5PrgdYi42XduuP69UVJZMCB-AbPXW2s</recordid><startdate>201306</startdate><enddate>201306</enddate><creator>Otebolaku, Abayomi M.</creator><creator>Andrade, Maria T.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201306</creationdate><title>Recognizing High-Level Contexts from Smartphone Built-In Sensors for Mobile Media Content Recommendation</title><author>Otebolaku, Abayomi M. ; Andrade, Maria T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-e0be6a3e3905e826027ad50cf2669f00929f0d9886d8977c77d7abcd1505e2bb3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Accelerometers</topic><topic>classification</topic><topic>Context</topic><topic>Context modeling</topic><topic>context recognition</topic><topic>low-level context data</topic><topic>Mobile communication</topic><topic>multimedia</topic><topic>Multimedia communication</topic><topic>Sensors</topic><topic>smartphone sensing</topic><topic>Support vector machine classification</topic><toplevel>online_resources</toplevel><creatorcontrib>Otebolaku, Abayomi M.</creatorcontrib><creatorcontrib>Andrade, Maria T.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore (Online service)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Otebolaku, Abayomi M.</au><au>Andrade, Maria T.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Recognizing High-Level Contexts from Smartphone Built-In Sensors for Mobile Media Content Recommendation</atitle><btitle>2013 IEEE 14th International Conference on Mobile Data Management</btitle><stitle>mdm</stitle><date>2013-06</date><risdate>2013</risdate><volume>2</volume><spage>142</spage><epage>147</epage><pages>142-147</pages><issn>1551-6245</issn><eissn>2375-0324</eissn><isbn>9781467360685</isbn><isbn>1467360686</isbn><eisbn>076954973X</eisbn><eisbn>9780769549736</eisbn><coden>IEEPAD</coden><abstract>Context Recognition is an important element for developing context aware mobile applications. However, context is mostly available as low-level sensor data that are in form not suitable for mobile applications. In this paper, we present a process that uses classifiers for recognizing high-level contexts from low-level sensor data. The process demonstrates accurate recognition of user activity contexts, using smart-phone built-in sensors. We describe and illustrate our context recognition model and then demonstrate its application in a context aware mobile multimedia recommendation system.</abstract><pub>IEEE</pub><doi>10.1109/MDM.2013.84</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1551-6245 |
| ispartof | 2013 IEEE 14th International Conference on Mobile Data Management, 2013, Vol.2, p.142-147 |
| issn | 1551-6245 2375-0324 |
| language | eng |
| recordid | cdi_ieee_primary_6569080 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Accelerometers classification Context Context modeling context recognition low-level context data Mobile communication multimedia Multimedia communication Sensors smartphone sensing Support vector machine classification |
| title | Recognizing High-Level Contexts from Smartphone Built-In Sensors for Mobile Media Content Recommendation |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T08%3A16%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Recognizing%20High-Level%20Contexts%20from%20Smartphone%20Built-In%20Sensors%20for%20Mobile%20Media%20Content%20Recommendation&rft.btitle=2013%20IEEE%2014th%20International%20Conference%20on%20Mobile%20Data%20Management&rft.au=Otebolaku,%20Abayomi%20M.&rft.date=2013-06&rft.volume=2&rft.spage=142&rft.epage=147&rft.pages=142-147&rft.issn=1551-6245&rft.eissn=2375-0324&rft.isbn=9781467360685&rft.isbn_list=1467360686&rft.coden=IEEPAD&rft_id=info:doi/10.1109/MDM.2013.84&rft_dat=%3Cieee_6IE%3E6569080%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=076954973X&rft.eisbn_list=9780769549736&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6569080&rfr_iscdi=true |