Recognising the Activities of Low Entropy Mobile People Using Wireless Proximity Data
To improve the functional decline among elderly people, understanding the human behaviour is a substantial approach. Human behaviour is complex in nature and a challenging task to understand by monitoring the daily life activities. A tiered approach is adopted and investigated in this research work...
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creator | Azam, M. A. Loo, J. Lasebae, A. Khan, S. K. A. Ejaz, W. |
description | To improve the functional decline among elderly people, understanding the human behaviour is a substantial approach. Human behaviour is complex in nature and a challenging task to understand by monitoring the daily life activities. A tiered approach is adopted and investigated in this research work to recognise the high level activities and behaviour of the target users in order to alleviate their health related activities. An algorithm is devised to detect low level tasks by using the contextual information (e.g. Bluetooth and Wi-Fi proximity data). These tasks are further utilized for recognition of high level activities. Different scenarios and experiments are conducted to investigate the problem in hand thoroughly. Using wireless proximity data for activity and behaviour detection also improves the understanding of important structures in social relationships. |
doi_str_mv | 10.1109/ITNG.2012.24 |
format | Conference Proceeding |
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Using wireless proximity data for activity and behaviour detection also improves the understanding of important structures in social relationships.</description><subject>Activity Recogniser</subject><subject>Contextual Data</subject><subject>Educational institutions</subject><subject>Entropy</subject><subject>Human Behaviour</subject><subject>Libraries</subject><subject>Population Entropy</subject><subject>Senior citizens</subject><subject>Task Analyser</subject><subject>Wireless communication</subject><subject>Wireless sensor networks</subject><subject>XML</subject><isbn>9781467307987</isbn><isbn>146730798X</isbn><isbn>9780769546544</isbn><isbn>0769546544</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj01Lw0AURUdEUGp37tzMH0icj5eZzLLUWgtRi7S4LJnktT5oMyEzqP33lurqcBf3cC9jd1LkUgr3sFi9znMlpMoVXLCxs6WwxhVgCoDLc5ZgrBbWlfaajWMkL5SxxpRW3rD1OzZh11GkbsfTJ_JJk-iLEmHkYcur8M1nXRpCf-QvwdMe-RJDf8L63PigAfcYI18O4YcOlI78sU71Lbva1vuI43-O2Opptpo-Z9XbfDGdVBk5kTJtTjOUgi1g63xdtk1bFNbqxhsUNRTt6R5oAa3xBWiQDo0vnfACEK3WoEfs_k9LiLjpBzrUw3FjlHBSa_0LjS1Q6A</recordid><startdate>201204</startdate><enddate>201204</enddate><creator>Azam, M. 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A.</creatorcontrib><creatorcontrib>Ejaz, W.</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>Azam, M. A.</au><au>Loo, J.</au><au>Lasebae, A.</au><au>Khan, S. K. A.</au><au>Ejaz, W.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Recognising the Activities of Low Entropy Mobile People Using Wireless Proximity Data</atitle><btitle>2012 Ninth International Conference on Information Technology - New Generations</btitle><stitle>itng</stitle><date>2012-04</date><risdate>2012</risdate><spage>109</spage><epage>114</epage><pages>109-114</pages><isbn>9781467307987</isbn><isbn>146730798X</isbn><eisbn>9780769546544</eisbn><eisbn>0769546544</eisbn><abstract>To improve the functional decline among elderly people, understanding the human behaviour is a substantial approach. Human behaviour is complex in nature and a challenging task to understand by monitoring the daily life activities. A tiered approach is adopted and investigated in this research work to recognise the high level activities and behaviour of the target users in order to alleviate their health related activities. An algorithm is devised to detect low level tasks by using the contextual information (e.g. Bluetooth and Wi-Fi proximity data). These tasks are further utilized for recognition of high level activities. Different scenarios and experiments are conducted to investigate the problem in hand thoroughly. Using wireless proximity data for activity and behaviour detection also improves the understanding of important structures in social relationships.</abstract><pub>IEEE</pub><doi>10.1109/ITNG.2012.24</doi><tpages>6</tpages></addata></record> |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Activity Recogniser Contextual Data Educational institutions Entropy Human Behaviour Libraries Population Entropy Senior citizens Task Analyser Wireless communication Wireless sensor networks XML |
title | Recognising the Activities of Low Entropy Mobile People Using Wireless Proximity Data |
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