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'''Background''' Currently, in the area of intelligent homes research, awareness of human behavior is an important research area. This area has for the last decades included non-trivial problems such as: activity recognition (what the person is doing), fall monitoring and in-door tracking of persons. However, the data acquisition phase for this kind of research is often a time-consuming, protracted and an expensive process which often is followed by the workload of maintaining the data set such as handling missing values. A complementary tool that helps in the process is a smart home simulator that is able to output sensor sequences similar to real sensor sequences. Such tool could be used for generating new data sets from models computed from previously collected data. Moreover, such generated sequences could be injected with anomalous sensor sequences to study the effect in several aspects such as: * Algorithms for outlier detection. * Algorithms for classifying different types of users. * Algorithms for visualizing data and analysis results. '''Project description''' The overall goal in this project is to develop a GUI-based intelligent home simulator able to output realistic sensor sequences given prior knowledge such as the planning of the home environment (possible from a CAD-file format), sensor positions and normal behavior of a person. The secondary, yet important goal, is to study, implement and validate algorithms for outlier detection using the generated data. The outlier detection algorithm should be compatible with the simulator previously developed at IS-lab. '''Activity plan''' The suggested project could be specified in the following work packages: '''WP1''' *Studying related work within the field of simulation, smart homes and outlier detection. *Writing a project plan. '''WP2''' *Develop a GUI-based smart home simulator in an appropriate programming language (or using an existing simulation tool). *Study and generate realistic anomalous sequences '''WP3''' *Studying, implementing and evaluating outlier detection methods able to detect injected anomalous sensor sequences.
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