{"id":{"repo_id":"nott-trent","oai_identifier":"oai:irep.ntu.ac.uk:285"},"canonical_url":"https://search.dev.ndltd.org/etd/nott-trent/oai:irep.ntu.ac.uk:285","repository":{"repo_id":"nott-trent","name":"Nottingham Trent University","base_url":"https://irep.ntu.ac.uk/cgi/oai2"},"display":{"title":"User profiling in the intelligent office","abstract":"The research aim is to investigate different methods of profiling user activities in an office environment. This will allow optimal use of resources in future Intelligent Office Environments while still taking account of user preferences and comfort. To achieve the goal of this research, a data collection system is designed and built. This required a wireless Sensor Network to monitor a wide range of ambient conditions and user activities, and a software agent to monitor user's Personal Computer activities. Collected data from different users are gathered into a central database and converted into a meaningful format for description of the worker's Activity of Daily Working (ADW) and office environment conditions. Different techniques including Approximate Entropy (ApEn), consistency measures, linear similarity measures and Dynamic Time Warping (DTW) are employed to quantify a user's behaviour and extract a user profile. The individual user profile is representative of a user's preferences, consisting of user routine activities, consistency of office usage and their thermal comfort. Using the statistical techniques, consistency and ApEn, it is possible to characterise different users with only a few parameters. Using similarity techniques one can assess the interrelationship of different aspects of a user's behaviour. This helps to assess the importance of those aspects within the profile.","abstract_html":"The research aim is to investigate different methods of profiling user activities in an office environment. This will allow optimal use of resources in future Intelligent Office Environments while still taking account of user preferences and comfort. To achieve the goal of this research, a data collection system is designed and built. This required a wireless Sensor Network to monitor a wide range of ambient conditions and user activities, and a software agent to monitor user's Personal Computer activities. Collected data from different users are gathered into a central database and converted into a meaningful format for description of the worker's Activity of Daily Working (ADW) and office environment conditions. Different techniques including Approximate Entropy (ApEn), consistency measures, linear similarity measures and Dynamic Time Warping (DTW) are employed to quantify a user's behaviour and extract a user profile. The individual user profile is representative of a user's preferences, consisting of user routine activities, consistency of office usage and their thermal comfort. Using the statistical techniques, consistency and ApEn, it is possible to characterise different users with only a few parameters. Using similarity techniques one can assess the interrelationship of different aspects of a user's behaviour. 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This will allow optimal use of resources in future Intelligent Office Environments while still taking account of user preferences and comfort. To achieve the goal of this research, a data collection system is designed and built. This required a wireless Sensor Network to monitor a wide range of ambient conditions and user activities, and a software agent to monitor user's Personal Computer activities. Collected data from different users are gathered into a central database and converted into a meaningful format for description of the worker's Activity of Daily Working (ADW) and office environment conditions. Different techniques including Approximate Entropy (ApEn), consistency measures, linear similarity measures and Dynamic Time Warping (DTW) are employed to quantify a user's behaviour and extract a user profile. The individual user profile is representative of a user's preferences, consisting of user routine activities, consistency of office usage and their thermal comfort. Using the statistical techniques, consistency and ApEn, it is possible to characterise different users with only a few parameters. Using similarity techniques one can assess the interrelationship of different aspects of a user's behaviour. This helps to assess the importance of those aspects within the profile."]},{"key":"dc:format","label":"Dc Format","values":["text"]},{"key":"dc:title","label":"Title","values":["User profiling in the intelligent office"]}]}],"canonical_facts":{"dc:creator":["Puteh, S"],"dc:date":["2013"],"dc:date.issued":["2013"],"dc:description.abstract":["The research aim is to investigate different methods of profiling user activities in an office environment. This will allow optimal use of resources in future Intelligent Office Environments while still taking account of user preferences and comfort. To achieve the goal of this research, a data collection system is designed and built. This required a wireless Sensor Network to monitor a wide range of ambient conditions and user activities, and a software agent to monitor user's Personal Computer activities. Collected data from different users are gathered into a central database and converted into a meaningful format for description of the worker's Activity of Daily Working (ADW) and office environment conditions. Different techniques including Approximate Entropy (ApEn), consistency measures, linear similarity measures and Dynamic Time Warping (DTW) are employed to quantify a user's behaviour and extract a user profile. The individual user profile is representative of a user's preferences, consisting of user routine activities, consistency of office usage and their thermal comfort. Using the statistical techniques, consistency and ApEn, it is possible to characterise different users with only a few parameters. Using similarity techniques one can assess the interrelationship of different aspects of a user's behaviour. This helps to assess the importance of those aspects within the profile."],"dc:format":["text"],"dc:identifier.uri":["https://irep.ntu.ac.uk/id/eprint/285/1/217211_Saifullizam.Puteh-2014.pdf"],"dc:language":["en"],"dc:publisher.institution":["Nottingham Trent University"],"dc:relation.isreferencedby":["https://irep.ntu.ac.uk/id/eprint/285/"],"dc:title":["User profiling in the intelligent office"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["doctoral"],"dc:type.qualificationname":["phd"]},"updated_at":"2026-07-24T06:30:47Z"}