User profiling in the intelligent office

• Main Author: Puteh, Saifullizam
• Format: Thesis
• Language: English
• Published: 2014
• Subjects: QA76 Computer software; QA71-90 Instruments and machines
• Online Access: http://eprints.uthm.edu.my/1275/1/24p%20SAIFULLIZAM%20PUTEH.pdf; http://eprints.uthm.edu.my/1275/

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. The novel contribution is the use of these techniques within the context of ADW. This research investigates soft computing techniques to enhance user profiling. A novel fuzzy characteristic matrix is proposed to summarised the ADW. The activity recognition models using an eventdriven and a fuzzy inference system are proposed to recognise a worker’s activities during times when the office is occupied and unoccupied during a workday. The experimental results demonstrate the models recognise a worker’s activities and can classify into six categories (home, lunch, short break, out of office duties, not use computer/lighting and use computer/lighting) with accuracy of more than 90%