| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 97 === Understanding a user’s preferences and then providing corresponding services is substantial in a smart home environment nowadays. On the other hand, reliable recognition of activities from cluttered sensory data is challenging and important as well for a smart home to provide more desirable services. Traditionally, preference learning and activity recognition for a smart home system were dealt with separately. In this thesis, we aim to develop a hybrid system which is the first trial to model the relationship of a preference model and an activity model so that the causal relation among activities and personal preferences can assist to recover the accuracy of activity recognition in the dynamic environment. Specifically, on-going activity which a user performs in this work is regarded as high level contexts to assist in learning the user’s preference model, Based on the learned model, the smart home system provides services to the user so that the hybrid system can better interact with the user and also gain his/her feedback to adjust the learned preference model. Afterwards, both of the activity model and preference model will be simultaneously adapted by the analysis of the feedback. In addition, we further design a multi-user hybrid system, which is extended from single-user hybrid system, to deal with the interactions among users in a multi-user environment. The experimental results are provided to show the effectiveness of the proposed approach.
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