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Enhancing context awareness with activity recognition and radio fingerprinting
Within context-aware computing, there is a growing interest in linking localization technologies with activity recognition in a cooperative way. Existing research works in this field face two main difficulties: lack of accuracy in their solutions and/or sophisticated hardware requirements. To avoid...
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creator | Martin, E. Shia, V. Posu Yan Kuryloski, P. Seto, E. Ekambaram, V. Bajcsy, R. |
description | Within context-aware computing, there is a growing interest in linking localization technologies with activity recognition in a cooperative way. Existing research works in this field face two main difficulties: lack of accuracy in their solutions and/or sophisticated hardware requirements. To avoid these issues, we present a light-weight, low-cost and high-accuracy system for localization and activity recognition, based on a smart phone and a single off-the-shelf wireless accelerometer attached to the waist. We process the accelerometer signal with the wavelet transform to precisely recognize different activities and obtain the velocity of the gait in real time. Additionally, we leverage the capabilities of the smart phone to accurately estimate locations making use of a multimode approach for radio fingerprinting. Eventually, we combine location information with activity recognition, observing a 9% improvement in the accuracy with which some activities are recognized. |
doi_str_mv | 10.1109/ICSC.2011.27 |
format | conference_proceeding |
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Existing research works in this field face two main difficulties: lack of accuracy in their solutions and/or sophisticated hardware requirements. To avoid these issues, we present a light-weight, low-cost and high-accuracy system for localization and activity recognition, based on a smart phone and a single off-the-shelf wireless accelerometer attached to the waist. We process the accelerometer signal with the wavelet transform to precisely recognize different activities and obtain the velocity of the gait in real time. Additionally, we leverage the capabilities of the smart phone to accurately estimate locations making use of a multimode approach for radio fingerprinting. Eventually, we combine location information with activity recognition, observing a 9% improvement in the accuracy with which some activities are recognized.</description><identifier>ISBN: 1457716488</identifier><identifier>ISBN: 9781457716485</identifier><identifier>EISBN: 0769544924</identifier><identifier>EISBN: 9780769544922</identifier><identifier>DOI: 10.1109/ICSC.2011.27</identifier><language>eng</language><publisher>IEEE</publisher><subject>Acceleration ; Accuracy ; activity recognition ; context awareness ; Digital TV ; Fingerprint recognition ; radio fingerprinting ; Smart phones ; Wavelet transforms</subject><ispartof>2011 IEEE Fifth International Conference on Semantic Computing, 2011, p.263-266</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/6061344$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6061344$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Martin, E.</creatorcontrib><creatorcontrib>Shia, V.</creatorcontrib><creatorcontrib>Posu Yan</creatorcontrib><creatorcontrib>Kuryloski, P.</creatorcontrib><creatorcontrib>Seto, E.</creatorcontrib><creatorcontrib>Ekambaram, V.</creatorcontrib><creatorcontrib>Bajcsy, R.</creatorcontrib><title>Enhancing context awareness with activity recognition and radio fingerprinting</title><title>2011 IEEE Fifth International Conference on Semantic Computing</title><addtitle>icsc</addtitle><description>Within context-aware computing, there is a growing interest in linking localization technologies with activity recognition in a cooperative way. Existing research works in this field face two main difficulties: lack of accuracy in their solutions and/or sophisticated hardware requirements. To avoid these issues, we present a light-weight, low-cost and high-accuracy system for localization and activity recognition, based on a smart phone and a single off-the-shelf wireless accelerometer attached to the waist. We process the accelerometer signal with the wavelet transform to precisely recognize different activities and obtain the velocity of the gait in real time. Additionally, we leverage the capabilities of the smart phone to accurately estimate locations making use of a multimode approach for radio fingerprinting. Eventually, we combine location information with activity recognition, observing a 9% improvement in the accuracy with which some activities are recognized.</description><subject>Acceleration</subject><subject>Accuracy</subject><subject>activity recognition</subject><subject>context awareness</subject><subject>Digital TV</subject><subject>Fingerprint recognition</subject><subject>radio fingerprinting</subject><subject>Smart phones</subject><subject>Wavelet transforms</subject><isbn>1457716488</isbn><isbn>9781457716485</isbn><isbn>0769544924</isbn><isbn>9780769544922</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotjc1KAzEURiMiaGt37tzkBTreO_lfylC1UHShrsttJmkDmpFMsPbtHdDVdzbnfIzdIDSI4O7W3WvXtIDYtOaMzcBop6R0rTxnM5TKGNTS2ku2GMe0A1RWtBrNFXte5QNln_Ke-yHX8FM5HamEHMaRH1M9cPI1fad64iX4YZ9TTUPmlHteqE8Dj5MayldJuU50zS4ifYxh8b9z9v6weuuelpuXx3V3v1kmNKouhVSw8xi9dpGiR2e1I6-sE63RANYjyAhROAM-RmnBCrI6eO3J9iEoMWe3f90UQthO759UTlsNGoWU4he9fk87</recordid><startdate>201109</startdate><enddate>201109</enddate><creator>Martin, E.</creator><creator>Shia, V.</creator><creator>Posu Yan</creator><creator>Kuryloski, P.</creator><creator>Seto, E.</creator><creator>Ekambaram, V.</creator><creator>Bajcsy, R.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201109</creationdate><title>Enhancing context awareness with activity recognition and radio fingerprinting</title><author>Martin, E. ; Shia, V. ; Posu Yan ; Kuryloski, P. ; Seto, E. ; Ekambaram, V. ; Bajcsy, R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-3450bc1fc69fafc19869ac5893276008c104f0f3970cff48083a86ec6ca8dee53</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Acceleration</topic><topic>Accuracy</topic><topic>activity recognition</topic><topic>context awareness</topic><topic>Digital TV</topic><topic>Fingerprint recognition</topic><topic>radio fingerprinting</topic><topic>Smart phones</topic><topic>Wavelet transforms</topic><toplevel>online_resources</toplevel><creatorcontrib>Martin, E.</creatorcontrib><creatorcontrib>Shia, V.</creatorcontrib><creatorcontrib>Posu Yan</creatorcontrib><creatorcontrib>Kuryloski, P.</creatorcontrib><creatorcontrib>Seto, E.</creatorcontrib><creatorcontrib>Ekambaram, V.</creatorcontrib><creatorcontrib>Bajcsy, R.</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 Digital Library</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>Martin, E.</au><au>Shia, V.</au><au>Posu Yan</au><au>Kuryloski, P.</au><au>Seto, E.</au><au>Ekambaram, V.</au><au>Bajcsy, R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Enhancing context awareness with activity recognition and radio fingerprinting</atitle><btitle>2011 IEEE Fifth International Conference on Semantic Computing</btitle><stitle>icsc</stitle><date>2011-09</date><risdate>2011</risdate><spage>263</spage><epage>266</epage><pages>263-266</pages><isbn>1457716488</isbn><isbn>9781457716485</isbn><eisbn>0769544924</eisbn><eisbn>9780769544922</eisbn><abstract>Within context-aware computing, there is a growing interest in linking localization technologies with activity recognition in a cooperative way. Existing research works in this field face two main difficulties: lack of accuracy in their solutions and/or sophisticated hardware requirements. To avoid these issues, we present a light-weight, low-cost and high-accuracy system for localization and activity recognition, based on a smart phone and a single off-the-shelf wireless accelerometer attached to the waist. We process the accelerometer signal with the wavelet transform to precisely recognize different activities and obtain the velocity of the gait in real time. Additionally, we leverage the capabilities of the smart phone to accurately estimate locations making use of a multimode approach for radio fingerprinting. Eventually, we combine location information with activity recognition, observing a 9% improvement in the accuracy with which some activities are recognized.</abstract><pub>IEEE</pub><doi>10.1109/ICSC.2011.27</doi><tpages>4</tpages></addata></record> |
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identifier | ISBN: 1457716488 |
ispartof | 2011 IEEE Fifth International Conference on Semantic Computing, 2011, p.263-266 |
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language | eng |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Acceleration Accuracy activity recognition context awareness Digital TV Fingerprint recognition radio fingerprinting Smart phones Wavelet transforms |
title | Enhancing context awareness with activity recognition and radio fingerprinting |
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