Evaluating a new classification method using PCA to human activity recognition
The ability to recognize human activities from sensed information is very important for ubiquitous computing applications using smart identification technologies. This paper addresses a new discriminative supervised method combining the classical Principal Components Analysis with the correlation cr...
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| creator | Abidine, M. B. Fergani, B. |
| description | The ability to recognize human activities from sensed information is very important for ubiquitous computing applications using smart identification technologies. This paper addresses a new discriminative supervised method combining the classical Principal Components Analysis with the correlation criterion for performing activity recognition in a smart home. We conduct several experiments, demonstrate the achievement of our method and show its promising results using real world dataset. |
| doi_str_mv | 10.1109/ICCMA.2013.6506158 |
| format | Conference Proceeding |
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B.</creatorcontrib><creatorcontrib>Fergani, B.</creatorcontrib><title>Evaluating a new classification method using PCA to human activity recognition</title><title>2013 International Conference on Computer Medical Applications (ICCMA)</title><addtitle>ICCMA</addtitle><description>The ability to recognize human activities from sensed information is very important for ubiquitous computing applications using smart identification technologies. This paper addresses a new discriminative supervised method combining the classical Principal Components Analysis with the correlation criterion for performing activity recognition in a smart home. We conduct several experiments, demonstrate the achievement of our method and show its promising results using real world dataset.</description><subject>activity recognition</subject><subject>Correlation</subject><subject>Hidden Markov models</subject><subject>Intelligent sensors</subject><subject>machine learning</subject><subject>Principal component analysis</subject><subject>sensors network</subject><subject>smart home</subject><subject>Smart homes</subject><subject>Ubiquitous computing</subject><isbn>1467352136</isbn><isbn>9781467352130</isbn><isbn>9781467352147</isbn><isbn>1467352144</isbn><isbn>9781467352123</isbn><isbn>1467352128</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1kMtOwzAURI0QElDyA7DxDyT42rFdL6Oo0ErlsYB1deM4rVEeKHaK-ve0oqxGMzoziyHkHlgGwMzjqixfiowzEJmSTIGcX5DE6DnkSgvJIdeX5PbfCHVNkhC-GGPHsuLc3JDXxR7bCaPvtxRp736obTEE33h7DIeedi7uhppO4US8lwWNA91NHfYUbfR7Hw90dHbY9v6E35GrBtvgkrPOyOfT4qNcpuu351VZrFMPWsZUiAZ4xSTmqkLdGCMU8LpRtallbpRC0A5dZQQiKGEt1IxXVlW5MVZIy8SMPPzteufc5nv0HY6HzfkC8Qvxp0_1</recordid><startdate>201301</startdate><enddate>201301</enddate><creator>Abidine, M. 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B. ; Fergani, B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-33f12b05a46ba7f993612df6d9d54966a17eaeb93aa163cc1d02bc6b499c35c03</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>activity recognition</topic><topic>Correlation</topic><topic>Hidden Markov models</topic><topic>Intelligent sensors</topic><topic>machine learning</topic><topic>Principal component analysis</topic><topic>sensors network</topic><topic>smart home</topic><topic>Smart homes</topic><topic>Ubiquitous computing</topic><toplevel>online_resources</toplevel><creatorcontrib>Abidine, M. B.</creatorcontrib><creatorcontrib>Fergani, B.</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 Electronic Library (IEL)</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>Abidine, M. B.</au><au>Fergani, B.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Evaluating a new classification method using PCA to human activity recognition</atitle><btitle>2013 International Conference on Computer Medical Applications (ICCMA)</btitle><stitle>ICCMA</stitle><date>2013-01</date><risdate>2013</risdate><spage>1</spage><epage>4</epage><pages>1-4</pages><isbn>1467352136</isbn><isbn>9781467352130</isbn><eisbn>9781467352147</eisbn><eisbn>1467352144</eisbn><eisbn>9781467352123</eisbn><eisbn>1467352128</eisbn><abstract>The ability to recognize human activities from sensed information is very important for ubiquitous computing applications using smart identification technologies. This paper addresses a new discriminative supervised method combining the classical Principal Components Analysis with the correlation criterion for performing activity recognition in a smart home. We conduct several experiments, demonstrate the achievement of our method and show its promising results using real world dataset.</abstract><pub>IEEE</pub><doi>10.1109/ICCMA.2013.6506158</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2013 International Conference on Computer Medical Applications (ICCMA), 2013, p.1-4 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | activity recognition Correlation Hidden Markov models Intelligent sensors machine learning Principal component analysis sensors network smart home Smart homes Ubiquitous computing |
| title | Evaluating a new classification method using PCA to human activity recognition |
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